Predictive Modeling in Public Health: The Role of AI

The integration of predictive modeling and artificial intelligence (AI) in public health represents a paradigm shift from reactive healthcare strategies to proactive, data-driven decision-making. This paper examines the foundational principles of predictive modeling, its evolution through AI techniques, and the diverse range of statistical, machine learning, and hybrid models applied within the public health sector. Drawing upon lessons from recent health crises, including the COVID-19 pandemic, the study highlights how AI enhances predictive capabilities in outbreak forecasting, resource allocation, and personalized medicine. Despite the transformative potential, challenges remain, ranging from data quality and interpretability to ethical and equity concerns. Through a synthesis of case studies, modeling methodologies, and future outlooks, this paper underscores the critical need for interdisciplinary collaboration, robust data infrastructures, and ethically grounded AI deployment to fully realize the benefits of predictive modeling in advancing global public health outcomes. Keywords: Artificial Intelligence, Predictive Modeling, Public Health, Machine Learning, Epidemiology, Health Forecasting, Big Data.