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This book explores the transformative role of machine learning (ML) in modern orthodontics, emphasizing how AI-driven technologies enhance diagnosis, treatment planning, and clinical outcomes. It introduces the basics of ML and deep learning, highlighting their capabilities in analyzing vast dental datasets and learning patterns from clinical cases. The book reviews various ML models-such as artificial neural networks, convolutional neural networks, and genetic algorithms-and their accuracy in tasks like cephalometric landmark detection, growth prediction, extraction decisions, and treatment outcome forecasting. It discusses the development of clinical decision support systems (CDSS) powered by ML, enabling more consistent and objective treatment recommendations. Numerous studies are reviewed to showcase ML's application in areas like facial analysis, skeletal maturity assessment, orthognathic surgery planning, and remote care. Despite challenges like data standardization and model generalization, ML holds strong promise in improving diagnostic precision and personalizing orthodontic care.