This book offers a comprehensive exploration of ensemble learning (EL), a powerful technique in machine learning and artificial intelligence. This book delves into the principles, methods, and practical applications of EL, emphasizing its significance in achieving robust and accurate predictive models. By integrating multiple models, EL outperforms single-model approaches, making it invaluable in diverse fields such as intelligent transportation systems, industrial monitoring, and smart homes. Through detailed case studies and real-world examples, the book illustrates how EL can be implemented in various scenarios, including the integration of heterogeneous data from edge computing devices within the AIoT framework. The discussion extends to advanced topics like the role of EL in 5G and edge AI, highlighting its potential to enhance performance in adverse conditions and time-sensitive applications. Written for practitioners and researchers, this book balances theoretical foundations with practical insights, encouraging hands-on experimentation and participation in data science competitions. It serves as an essential guide for those seeking to leverage EL's full potential, offering strategies to address real-world challenges and advance AI applications. Whether you are a seasoned data scientist or a newcomer to the field, "Ensemble Learning: Theory and Applications" provides the knowledge and tools to harness the power of ensemble methods effectively.