Machine Learning and Blockchain – Challenges, Future Trends and Sustainable Technologies

With contributions from leading researchers and industry experts, this book examines cutting-edge applications, integration models, and sustainable solutions across sectors including finance, agriculture, healthcare, IoT, and smart cities. Chapters cover blockchain-enabled fintech operations, fraud detection, deep learning–driven intrusion detection, AI-enhanced smart contracts, and data-driven healthcare innovations. Case studies, methodologies, and future-oriented insights demonstrate how these technologies can foster secure, efficient, and sustainable ecosystems. By bridging theoretical foundations with practical implementations, this book offers readers a roadmap to navigate the opportunities and challenges shaping the next generation of intelligent, blockchain-powered systems.

Key Features:

-Integrates blockchain with machine learning for real-world applications.
-Applies advanced analytics, automation, and AI models to enhance blockchain ecosystems.
-Develops secure solutions in fintech, agriculture, healthcare, IoT, and smart cities.
-Evaluates case studies and frameworks addressing challenges and vulnerabilities.
-Explores sustainable, future-ready trends shaping intelligent systems.