This book offers a comprehensive exploration of utilizing artificial intelligence (AI) for transformer health assessment, providing readers with both foundational knowledge and advanced techniques. It begins with an introduction to transformer components and the importance of health monitoring, highlighting the limitations of traditional methods. The text then delves into AI fundamentals, including machine learning types and relevant techniques, followed by detailed discussions on data collection, preprocessing, and feature extraction. Predictive maintenance models and anomaly detection are covered extensively, showcasing various AI-driven approaches. Advanced sections focus on deep learning, time-series analysis, and hybrid models, emphasizing practical implementation and integration with existing systems. Real-world case studies illustrate the application and impact of AI on transformer maintenance, while future trends and research directions are explored. Concluding with a summary of transformative effects on maintenance practices, the book provides a thorough understanding of how AI can enhance transformer health monitoring and management, offering valuable insights for both practitioners and researchers in the field.