Machine learning (ML) is transforming personalized medicine by creating data-driven treatments based on genetics, medical history, and lifestyle. ML models analyze large data sets, including genomic data, health records, and medical imaging, to improve disease diagnosis, predict medication responses, and customize treatments. Techniques like deep learning and natural language processing (NLP) are crucial for modeling symptoms, personalizing therapies, and aiding clinical decisions. In oncology, ML helps discover biomarkers for targeted therapies and reduces trial-and-error treatment, while in cardiology, AI-based models predict cardiovascular risk with high accuracy. Continuous monitoring via wearables and IoT enables real-time health assessment, improving care and prevention. Challenges include data privacy, algorithm bias, and regulatory issues. Ethical AI use, patient confidentiality, and model explainability are vital for widespread adoption. Future steps involve integrating wearable tech with real-time clinical applications to further personalize medicine, making healthcare more precise and accessible. This review explores ML advancements in healthcare, evaluating future research directions to address limitations and open new pathways for care.