Predictive modeling has emerged as a transformative approach in disease prevention, enabling healthcare systems to identify high-risk individuals and populations before disease onset. Advances in artificial intelligence (AI), machine learning (ML), big data analytics, and electronic health records (EHRs) have enhanced the ability to predict disease occurrence, progression, and outcomes with increasing accuracy. Predictive models utilize demographic, genetic, behavioral, environmental, and clinical data to support personalized prevention strategies and population health management. This review explores the principles, methodologies, applications, benefits, limitations, and future directions of predictive modeling for disease prevention. The findings suggest that predictive analytics can significantly improve early intervention, reduce healthcare costs, optimize resource allocation, and enhance public health outcomes.