Machine Learning in Biomedical and Health Informatics is a comprehensive guide that explores the transformative role of machine learning in healthcare. It begins with an introduction to ML, laying the groundwork by defining its scope, importance, and the types of learning methods. The book progresses to cover critical topics such as data preprocessing, supervised and unsupervised learning, deep learning, and natural language processing (NLP). Chapters delve into advanced applications like reinforcement learning, wearable technologies, and the integration of ML into clinical workflows. Ethical and privacy considerations are given due emphasis, reflecting the importance of trust and security in healthcare. Practical challenges and strategies for deploying ML models in real world settings are thoroughly discussed, ensuring readers understand the nuances of transitioning from theory to practice. Furthermore, the book explores future trends such as Explainable AI, federated learning, and emerging ML tools in biomedical research. Each chapter is structured to provide theoretical foundations, practical insights, and illustrative case studies, making complex concepts accessible to a broad audience. Whether you are a data scientist, clinician, or healthcare policymaker, this book equips you with the knowledge to harness ML’s potential. It empowers readers to address current challenges and envision innovative solutions that can revolutionize the delivery of healthcare services globally.