The purpose of this book is to serve as a forum in which academics and researchers from all over the globe may come together to communicate, promote, and debate numerous new challenges and advancements in many fields related to engineering applications and machine learning (ML). Machine learning is a part of artificial intelligence and consists of machines learning from real data without being directly programmed. Therefore, this comprehensive work explores the rapidly evolving integration of machine learning and automation technologies across several engineering disciplines. As our world becomes more digital and networked, the engineering industry spearheads innovation using advanced machine learning algorithms and automated systems to revolutionize traditional practices. This book examines sophisticated applications, such as predictive maintenance in manufacturing, autonomous control systems in different engineering applications, and smart grids in electrical engineering. It provides an in-depth analysis of how automation, driven by advanced machine learning models, enhances processes, augments safety, and boosts efficiency in real-world engineering applications. This book analyses this technological shift's ethical ramifications and challenges, including the balance between human oversight and machine autonomy, data privacy concerns, and potential societal repercussions. This book also explores how to use machine learning to create smart health monitoring systems and smart agricultural solutions. This book presents a forward-thinking perspective on the impact of automation and machine learning on the future of engineering and its ramifications for global industries. It targets engineering professionals, researchers, students, technology specialists, and specific themed collections reflecting the latest developments in machine learning research. This book presents machine learning for fleet management, which helps in geolocation, performance analysis, telemetry co