Artificial intelligence and predictive technology are revolutionizing healthcare by enabling more accurate diagnoses, personalized treatment plans, and proactive patient care. They can forecast disease progression, predict patient readmission risks, identify individuals at high risk for conditions like sepsis or heart failure, and optimize treatment protocols based on individual patient characteristics. By shifting healthcare from a reactive to a predictive model, these technologies not only improve patient outcomes and reduce mortality rates but also significantly lower healthcare costs by preventing complications and reducing unnecessary procedures, ultimately creating a more efficient and effective healthcare ecosystem. Artificial Intelligence for Predictive Healthcare: Towards Personalized Treatment and Disease Prevention delves into the algorithms, technologies, and applications that are driving this transformation of healthcare. Highlights include: Optimizing diagnosis and treatment plans with AI Machine learning and generative AI for cancer diagnosis and treatment The evolving role of healthcare professionals in smart healthcare Hybrid machine learning algorithms for early prediction of diabetes Bringing together the perspectives of professionals, researchers, and practitioners working at the intersection of technology and healthcare, the book reflects a shared belief that AI's role in healthcare is not just about algorithms and data, but about improving lives. From predicting disease outbreaks to creating tailored treatment plans, the book covers a range of applications. With real-world examples and case studies, it offers a roadmap to understanding AI's potential to predict, personalize, and prevent health conditions.