Predictive Models in Autism Spectrum Disorder: A Machine Learning Perspective explores how artificial intelligence and data science are transforming autism research, diagnosis, and long-term care. As Autism Spectrum Disorder (ASD) continues to rise globally, traditional diagnostic methods—often time-consuming and subjective—struggle to meet growing clinical demands. This book presents a forward-looking, data-driven approach to understanding and predicting ASD through machine learning models. From foundational data science concepts to advanced deep learning applications, this book provides a structured and practical guide to building predictive models using behavioral, genetic, neuroimaging, and clinical datasets. Readers will learn how algorithms such as Decision Trees, Random Forests, Support Vector Machines, and Neural Networks are applied to early diagnosis, treatment personalization, and adult vocational support.Beyond technical frameworks, the book also addresses real-world implementation, ethical challenges, algorithmic bias, privacy concerns, and the future of personalized medicine in autism care. Through case studies, data analysis examples, and interdisciplinary insights, this work bridges the gap between research and clinical practice. Whether you are a student, researcher, clinician, or data scientist, this book offers both theoretical depth and practical tools to understand how predictive intelligence can reshape autism care for the better.