AI and Machine Learning

Welcome Message

We are delighted to invite you to the “7^th International Congress on AI and Machine Learning,” taking place on June 18-19, 2026 in Paris, France. Under the theme “Next-Generation Artificial Intelligence: From research to real-world impact,” the congress will showcase the latest breakthroughs and emerging trends in artificial intelligence and machine learning.

This event will bring together researchers, industry leaders, academicians, and innovators to present cutting-edge research, explore practical applications, and discuss the challenges and opportunities shaping the AI landscape. The program features keynote sessions, technical presentations, and interactive discussions designed to foster collaboration and knowledge exchange.

Join us for an engaging and inspiring experience as we work together to advance intelligent technologies and create meaningful global impact.

Target Audience:

• Artificial Intelligence and Machine Learning Researchers
• Data Scientists and Data Analysts
• Computer Science and Engineering Professionals
• Software Developers and AI Solution Architects
• Industry Leaders and Technology Executives
• Academicians and University Professors
• Postdoctoral Fellows and Research Scholars
• PhD, Master’s, and Undergraduate Students
• Robotics and Automation Specialists
• Business Intelligence and Analytics Experts
• Cybersecurity Professionals
• Healthcare AI and FinTech Innovators
• Startups and Entrepreneurs in AI-driven sectors
• Policy Makers and AI Ethics Specialists
• Technology Consultants and Digital Transformation Experts

About Conference

Join us for the “7^th International Congress on AI and Machine Learning, held on June 18–19, 2026 in Paris, France”. This year’s theme, “Next-Generation Artificial Intelligence: From Research to Real-World Impact,” highlights the latest innovations and transformative applications in AI and Machine Learning.

The congress brings together researchers, industry leaders, academicians, and innovators from around the world to share knowledge, present cutting-edge research, and explore practical solutions. The program features keynote addresses, oral and poster presentations, workshops, and interactive discussions, creating an engaging environment for learning and networking.

Attendees will have the opportunity to connect with global AI pioneers, showcase their research, explore emerging technologies, and gain insights from distinguished speakers. Participate in dynamic discussions that advance the field and help shape the future of intelligent technologies worldwide.

Why to Attend

ARTIFICIAL INTELLIGENCE-2026 is the ideal platform to connect with a global community of AI researchers, industry experts, and technology innovators. The conference will showcase the latest trends and breakthroughs in artificial intelligence, machine learning, deep learning, robotics, data science, and ethical AI practices, highlighting their real-world applications across industries.

During the two-day event, participants will have the opportunity to present their research, exchange ideas, and engage in interactive sessions with leading experts. Whether you are a seasoned professional, academician, or emerging researcher, Artificial Intelligence 2026 offers a valuable opportunity to learn, network, and collaborate on innovative solutions that are shaping the future of intelligent technologies worldwide.

Sessions & Tracks

Track 1: Artificial Intelligence

Artificial Intelligence (AI) focuses on building intelligent systems capable of reasoning, learning, perception, and autonomous decision-making. It integrates machine learning, computer vision, natural language processing, robotics, and knowledge representation. AI is transforming industries such as healthcare, finance, transportation, manufacturing, and education. Current research emphasizes scalable AI systems, trustworthy AI, and real-world deployment. Ethical governance and responsible innovation remain central to AI’s evolution.

Track 2: Machine Learning

Machine Learning (ML) develops algorithms that learn from data and improve performance without explicit programming. It includes supervised, unsupervised, semi-supervised, and reinforcement learning paradigms. ML powers predictive analytics, recommendation engines, fraud detection, and intelligent automation. Advances in scalable training, model deployment (MLOps), and real-time inference are driving enterprise adoption. Data quality and model generalization remain critical research areas.

Track 3: Deep Learning

Deep Learning leverages multi-layer neural networks to analyze complex structured and unstructured data. Architectures such as CNNs, Transformers, graph neural networks, and diffusion models dominate modern AI systems. Applications include speech recognition, computer vision, autonomous vehicles, medical diagnostics, and generative AI. Ongoing innovations focus on efficiency, scalability, interpretability, and reducing computational cost. The growth of foundation models and large-scale pretraining continues to expand deep learning capabilities across industries.

• Convolutional Neural Networks (CNNs)
• Generative Deep Learning Models
• Graph Neural Networks (GNNs)
• Transformer Architectures & Attention Mechanisms

Track 4: AI in Cybersecurity

AI enhances cybersecurity through intelligent threat detection, anomaly identification, malware classification, and automated response systems. Machine learning models detect evolving cyber threats in real time. Applications include fraud detection, behavioral analytics, phishing prevention, and zero-day attack detection. Research also explores adversarial robustness and AI-driven defense strategies. AI is becoming a cornerstone of proactive cyber resilience.

Track 5: Natural Language Processing

Natural Language Processing (NLP) enables machines to understand, interpret, and generate human language. Applications include conversational AI, sentiment analysis, speech recognition, translation, and document intelligence. Large Language Models (LLMs) have revolutionized NLP with contextual reasoning and generative capabilities. Research focuses on multilingual systems, bias mitigation, and scalable language understanding. NLP bridges human communication and intelligent systems.

Track 6: Internet of Things (IoT) & Edge AI

Internet of Things (IoT) connects physical devices for real-time data collection and communication. AI enhances IoT systems with intelligent analytics and automated decision-making. Edge AI processes data locally to reduce latency and enhance privacy. Applications include smart cities, industrial IoT, healthcare monitoring, and predictive maintenance. The convergence of AI and IoT enables intelligent ecosystems.

• IoT Security, Privacy & Trust
• Smart Agriculture & Environmental Intelligence
• Smart Cities & Urban Intelligence
• Industrial IoT (IIoT) & Smart Manufacturing

Track 7: Artificial Neural Networks

Artificial Neural Networks (ANNs) form the foundation of modern AI systems. Inspired by biological neurons, ANNs learn patterns through weighted connections and backpropagation. The power speech recognition, financial modeling, image classification, and predictive systems. Research focuses on improving interpretability, robustness, and training efficiency. Advanced neural architectures continue to evolve AI capabilities.

Track 8: Cloud Computing for AI

Cloud computing provides scalable, on-demand infrastructure to accelerate AI model development, training, and deployment. It supports high-performance computing, distributed storage, GPU/TPU acceleration, and AI-as-a-Service platforms, enabling real-time analytics and large-scale enterprise applications. Cloud-native AI architectures facilitate global collaboration, while MLOps integration ensures continuous model deployment, monitoring, and lifecycle management. Leading platforms like Amazon Web Services, Microsoft Azure, and Google Cloud drive innovation with managed AI services and hybrid cloud solutions.

• Security, Privacy & Responsible AI in the Cloud
• AI-as-a-Service (AIaaS) Platforms
• MLOps & Model Lifecycle Management
• Edge-Cloud Integration

Track 9: Big Data & Data Engineering

Big Data powers AI by providing vast amounts of structured and unstructured information for analysis and model training. Data engineering ensures the efficient collection, cleaning, transformation, and processing of these datasets through scalable pipelines. Distributed storage and high-performance computing frameworks enable the handling of high-volume, high-velocity data streams. AI-driven analytics then extract actionable insights to support strategic decision-making, optimization, and predictive modeling. Robust data management, governance, and quality control remain critical, as data continues to serve as the foundation of AI innovation, driving smarter, faster, and more reliable intelligent systems across industries.

Track 10: Applied Machine Learning Across Industries

Applied Machine Learning drives AI innovation across healthcare, finance, retail, logistics, and manufacturing. It powers predictive maintenance, intelligent automation, personalization, fraud detection, and decision-support systems. Focused on scalability, measurable ROI, and seamless workflow integration, these solutions bridge research and real-world deployment. Cross-domain applications highlight ML’s versatility, transforming data into actionable insights that improve efficiency, customer experience, and strategic decision-making across industries.

Track 11: Robotics and Intelligent Automation

Robotics combines AI with mechanical systems to enable autonomous perception, navigation, and manipulation. Applications span industrial robots, collaborative robots (cobots), healthcare robotics, and autonomous drones. AI techniques like reinforcement learning and computer vision enhance adaptability, learning, and efficiency. Robotics is at the forefront of smart automation, transforming industries with intelligent, interactive, and adaptive machines.

• Industrial & Collaborative Robots
• Healthcare Robotics
• Autonomous Drones & Vehicles
• AI & Machine Learning for Robotics
• Human-Robot Interaction & Safety

Track 12: Neural Network Optimization

Neural Network Optimization aims to enhance the performance, efficiency, and scalability of AI models for diverse applications. Core techniques include hyperparameter tuning, neural architecture search (NAS), pruning, quantization, and model compression, which reduce computational load and energy consumption. Optimized networks can be deployed on edge devices and resource-constrained environments. Research continues to balance accuracy, speed, and efficiency, enabling sustainable, cost-effective, and high-performing AI systems across industries.

Track 13: Reinforcement Learning Applications

Reinforcement Learning (RL) trains intelligent agents to make sequential decisions by learning from rewards and penalties. By combining RL with deep learning, agents can operate effectively in complex, high-dimensional environments. RL is widely applied in robotics, autonomous systems, gaming AI, supply chain optimization, recommendation systems, and adaptive control. Research focuses on improving stability, sample efficiency, and generalization to real-world scenarios. Modern RL approaches enable agents to learn from interaction, adapt to dynamic conditions, and optimize long-term outcomes. This track highlights both theoretical advances and practical deployments of RL across industries and emerging applications.

Track 14: Quantum Machine Learning

Quantum Machine Learning (QML) merges quantum computing with machine learning to accelerate computations, optimize complex problems, and simulate systems beyond classical capabilities. By leveraging quantum principles such as superposition and entanglement, QML can process high-dimensional data, enhance pattern recognition, and improve optimization in AI tasks. Applications span chemistry simulations, financial modeling, drug discovery, and large-scale AI problem-solving. Research focuses on developing quantum algorithms, hybrid quantum-classical models, and efficient quantum hardware. QML represents the next frontier of AI, enabling breakthroughs in speed, accuracy, and complexity that traditional computing alone cannot achieve.

• Quantum Algorithms for Machine Learning
• Hybrid Quantum-Classical Models
• Quantum Hardware & Optimization
• Theoretical Foundations & Research

Track 15: Responsible & Ethical AI

Responsible and Ethical AI emphasizes fairness, transparency, accountability, and privacy in AI systems. It addresses challenges such as algorithmic bias, explainability, and adherence to regulatory and legal standards. Ethical frameworks guide the deployment of AI across sensitive domains including healthcare, finance, governance, and public services. Research focuses on building trust, designing human oversight mechanisms, and ensuring AI aligns with societal values. By promoting responsible innovation, organizations can deploy AI technologies safely, inclusively, and sustainably. This track explores strategies, frameworks, and practical approaches to ensure AI benefits are maximized while minimizing harm and ethical risks.

Track 16: Multi-Agent Systems

Multi-Agent Systems (MAS) consist of multiple intelligent agents interacting within shared environments, either collaboratively or competitively. These systems are applied in traffic management, distributed robotics, smart grids, resource allocation, and simulation of complex socio-technical systems. MAS rely on coordination, negotiation, and decentralized decision-making to achieve collective goals while adapting to dynamic conditions. Reinforcement learning and other AI techniques enhance both cooperative and competitive strategies among agents. Research in MAS focuses on scalability, robustness, and emergent behavior, enabling the design of intelligent, distributed ecosystems where agents autonomously optimize outcomes, improve efficiency, and tackle complex, real-world problems.

Track 17: Generative Adversarial Networks & Diffusion Models

Generative Adversarial Networks (GANs) and diffusion models create realistic images, videos, audio, and synthetic data. They enable content creation, data augmentation, simulations, and virtual environments. Research focuses on improving training stability, sample efficiency, and output quality. These generative models are transforming digital media, entertainment, healthcare, and AI-driven simulations, driving innovation across creative and industrial applications.

• GAN Architectures & Techniques
• Diffusion Models & Score-Based Generative AI
• Applications in Media & Design
• Synthetic Data & Simulation

Track 18: Computer Vision

Computer Vision empowers machines to perceive, analyze, and interpret visual data for actionable insights. Applications include facial recognition, object detection, video surveillance, autonomous vehicles, and medical imaging. Advances in deep learning have dramatically improved accuracy, efficiency, and real-time processing. Emerging trends such as 3D vision, multimodal learning, and vision-language models expand capabilities. Computer vision systems are pivotal in automation, security, robotics, and augmented reality, enabling smarter decision-making and enhanced human-machine interaction across industries.

Track 19: Predictive Analytics

Predictive Analytics leverages statistical modeling and machine learning to forecast trends, behaviors, and future outcomes. Key applications include risk management, supply chain optimization, demand forecasting, fraud detection, and customer behavior analysis. Advanced time-series models and AI-powered forecasting tools enable accurate predictions and informed strategic decision-making. Integration with real-time analytics and diverse data sources enhances operational efficiency. By transforming historical and current data into actionable insights, predictive analytics helps organizations anticipate challenges, optimize performance, and maintain a competitive advantage across industries, driving smarter, data-driven decision-making at scale.

Track 20: AI Futures & Emerging Trends

AI is advancing with innovations such as autonomous agents, AI copilots, synthetic data, digital twins, and AI-driven scientific discovery. These technologies are shaping the long-term impact of AI on industry and society, emphasizing scalable foundation models, cross-domain intelligence, and transformative applications. Research focuses on sustainable, human-centric AI design, ethical deployment, and maximizing real-world value. By integrating cutting-edge methods with practical solutions, organizations and researchers can leverage AI to drive innovation, efficiency, and societal benefits, paving the way for intelligent systems that are responsible, adaptive, and impactful across multiple sectors.

Track 21: Neuromorphic Computing & Brain-Inspired AI

Neuromorphic Computing designs AI systems inspired by the human brain, using spiking neural networks, event-based sensors, and bio-inspired architectures. It enables energy-efficient, real-time learning on edge devices and adaptive intelligence for perception, memory, and reasoning. This approach advances low-power AI, cognitive computing, and autonomous systems.

• Neuromorphic Hardware & Edge AI
• Event-Based & Sensor-Driven AI
• Applications in Robotics & Autonomous Systems
• Adaptive Learning & Cognitive Computing

Track 22: Advanced Deep Learning Architectures

Explores cutting-edge neural network designs including transformers, graph neural networks (GNNs), capsule networks, and attention-based models. Focuses on scalable, efficient architectures, model optimization, and applications across NLP, computer vision, and multimodal AI. Covers interpretability, robustness, and energy-efficient deployment, enabling high-performance AI systems suitable for both cloud and edge environments.

Track 23: AI-Driven Autonomous Systems & Robotics

Focuses on intelligent autonomous systems and advanced robotics, including multi-robot coordination, adaptive control, and reinforcement learning in dynamic, real-world environments. Topics include navigation, perception, human-robot interaction, and self-learning agents capable of decision-making and task adaptation. Applications span logistics, manufacturing, defense, exploration, and service robotics. Emphasizes scalable deployment, robust performance, and integration of AI for safe, efficient, and intelligent autonomous operations.

• Multi-Robot Coordination & Swarm Intelligence
• Adaptive & Self-Learning Robotics
• Applications & Deployment
• Human-Robot Interaction

Market Analysis

In 2026, the global Artificial Intelligence landscape continues to witness remarkable expansion, driven by widespread adoption of AI technologies across industries, accelerated advancements in machine learning research, and an increasing demand for global knowledge-sharing platforms. With the AI and Machine Learning market projected to reach USD 225.91 billion by 2030, growing at a CAGR of 36.2%, industry conferences have become more essential than ever in fostering collaboration, presenting breakthrough innovations, and shaping the future of intelligent systems.

Market Size and Growth

By 2026, the AI and Machine Learning conference segment reflects the dynamic growth of the broader AI industry. The conference market, which was valued at approximately USD 2.8 billion in 2025, continues to expand steadily, supported by strong participation from researchers, technology leaders, policymakers, startups, and enterprise innovators. The sustained annual growth rate of 25–30% highlights the increasing importance of professional forums dedicated to AI-driven transformation.

Globally, the number of AI and ML conferences in 2026 is expected to surpass 2,000 annual events, underscoring the rapid expansion of AI ecosystems worldwide. Regions such as Asia-Pacific and the Middle East have emerged as influential hubs for AI gatherings, fueled by proactive government initiatives, national AI strategies, and substantial private-sector investments in digital infrastructure.

The hybrid conference model continues to dominate in 2026, seamlessly integrating in-person and virtual participation to enhance global accessibility and engagement. Strong corporate sponsorships, strategic partnerships with academic institutions, and cross-industry collaborations further elevate the scale, impact, and visibility of these events.

Moreover, specialized themes including Generative AI, Ethical AI, Responsible AI governance, AI for Social Good, AI in Healthcare, Autonomous Systems, and Edge AI are shaping the conference landscape. This diversification not only reflects evolving industry priorities but also attracts a broader and more interdisciplinary audience.

In 2026, AI conferences stand as critical platforms for accelerating innovation, strengthening global networks, and driving the responsible advancement of intelligent technologies worldwide.

Benefits of Participation

Benefits of Participation- Speaker

• Worldwide acknowledgment of Researcher’s profile
• Make Lasting connections at Networking and Social Events
• An opportunity to give One-page advertisement in abstract book and flyers distribution which eventually gets 1 Million views and add great value to your research profile
• Learn beyond your field of interest, a change to know more about the new topics and research apart from your core subject from ARTIFICIAL INTELLIGENCE.
• We provide unique convergence of Networking, Learning and Fun into a single package

Benefits of Participation- Delegate

• Professional Development –Uplift the knowledge and skills
• Conference attendance inspires, rejuvenates, and energizes delegates
• Your participation at our conference will be helpful for a new approach and ideology that can be utilized for the extending the outcome of companies or industries.
• Opportunities to meet through online webinar for ARTIFICIAL INTELLIGENCE researchers and experts of same field and share new ideas

Benefit of Participation- Sponsor

• Exposure to the international atmosphere will increase the odds of getting new business
• Opportunity to showcase the new technology, new products of your company, or the service your industry to a broad international participant.
• World’s No. 1 platform to show case ARTIFICIAL INTELLIGENCE products.
• Increase business by lead generation through our conference participants.
• ARTIFICIAL INTELLIGENCE conferences create opportunities for greater focus and reflection that could help you take your business to the next level.
• Benchmarking key strategies for business and moving it forward
• Real Benefits in New business-Many Organizations make deals and sign contracts at our ARTIFICIAL INTELLIGENCE.

Benefit of Association for Collaborators

• No one in the world have this huge visitor towards ARTIFICIAL INTELLIGENCE, this is the best platform to showcase the society
• Create long-lasting relationships with the peers
• Promotional content and Logo of your Association at our conference banner, website and other proceedings, branding and marketing material will increase your subscribers/Members number by 40%.
• Our event visibility to your Organization page can give a great impact for your association in the Global Market forum.
• Your representatives can network with key conference delegates to update their knowledge and understanding of your organization and services.
• Details will be incorporated with ARTIFICIAL INTELLIGENCE promotional materials like flyers, brochure, pamphlets, program which will be distributed to Hospitals, Universities, Society and Researchers.

Visa Trip Advisor

Planning a trip to Paris, Frace? Attend our Meeting!

Issue with VISA?

To support participants in their Visa Application Process, we provide VISA support documents as follows:

1. Official Letter of Invitation
2. Official Letter of Abstract Acceptance
3. Receipt of Payment

Points to note:

• Visa Letter (official letter of invitation) will be issued only after successful registration and payment for the conference.
• Visa Letters can be issued only for the individual accepted to attend the conference.
• Please contact the Program Manager – Alice Mary ,via machinelearning@memeetings.com to arrange for a Visa Letter.

Kindly provide us with the following information for Visa Letters:

• Your name as it appears on your passport
• Passport Scan Copy (passport number and date of birth)
• Abstract Acceptance letter

Payment Methods:

1. Payment Gateway - RAZORPAY
2. Bank-to-Bank transfer

Having trouble with registration?

Please contact Program Manager – Alice Mary via machinelearning@memeetings.com. The ARTIFICIAL INTELLIGENCE-2026, team will provide you with an INVOICE for the requested price, enabling you to make the Bank-to-Bank transfer.