This research focuses on the role of big data and AI in improving health and medical and public health management. The study is expected to ensure that large datasets and state-of-art analysis are used to increase precision, the speed of interventions and their efficacy with an overall focus on global health. At Insight, we used four major variants of AI – Random Forests, Support Vector Machines, Neural Net, and K Means to configure medical data and thereby optimize personalization of treatment for diseases with 92% efficacy. Consequently, our findings accentuate that AI based technique can perform more proficiently than heuristic technique, most notably, in vast dataset management. The study also shows the concerns of data quality and ethic in using AI in health care. The big data with AI integration successfully worked in the identification of the healthcare disparities, accurate identification of diseases, and the successful formulation of the public health strategies. These studies imply that when AI is used critically and safely, it may contribute towards the improvement of patient care and gain better societal health administration.