Personalized medicine aims to tailor disease prevention, diagnosis, and treatment to individual patients by leveraging genetic, clinical, and lifestyle data. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for rapidly analyzing complex biomedical datasets, thereby advancing precision healthcare. This chapter explores the applications, methodologies, challenges, and future directions of AI/ML in personalized medicine. Key AI techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning, are being applied to diverse data sources such as genomics, electronic health records (EHRs), medical imaging, and wearable devices. These models enhance disease diagnosis, risk prediction, and personalized treatment strategies, particularly in fields like oncology, cardiology, and neurology. Additionally, AI is transforming drug discovery, clinical trial optimization, and precision public health initiatives. However, challenges such as data privacy, algorithmic bias, model interpretability, and regulatory constraints must be addressed. Ethical concerns are also discussed, alongside potential solutions like explainable AI (XAI) and federated learning (FL). Looking ahead, future trends include AI-powered digital twins, multi-omics data integration, and real-time patient monitoring systems. As AI continues to evolve, it promises to drive personalized medicine toward more precise, efficient, and scalable healthcare solutions.