The rapid expansion of health-related data, driven by wearable devices, mobile applications, and electronic health records (EHRs), is transforming modern healthcare. This growing influx of personalized data presents a significant opportunity to enhance individual well-being and support preventive care. However, effectively leveraging this information to provide personalized health insights remains a considerable challenge. Machine learning (ML) has emerged as a powerful tool in addressing this issue, offering the potential to predict health risks, identify patterns in patient behavior, and enable proactive health management. This paper reviews advanced ML techniques and their current applications in forecasting health risks, detecting early signs of disease, and facilitating personalized interventions. It also explores the critical challenges in implementing ML solutions, such as data quality, privacy concerns, integration with existing healthcare systems, and the need for model transparency. Through a comprehensive analysis of existing methods, this study demonstrates how ML can revolutionize healthcare by supporting timely and customized care decisions. The findings underscore the transformative potential of ML in promoting better health outcomes, reducing the burden on healthcare systems, and shifting the focus from reactive treatment to proactive, patient-centered care.