Artificial Intelligence (AI) and metaheuristic optimization have had a multidimensional influence on public health through personalizing sustainable disease detection, optimization of resources, and prevention care. AI and metaheuristic optimization enable early disease detection, optimization of healthcare facilities, and personalized intervention, and this improves patient outcomes and operating efficiency. Machine learning (ML) and metaheuristic algorithms have proved to be a vast potential solution to the complexity of the public health system, especially in resource-limited settings, in an effort to achieve long-term sustainability. Application of AI in healthcare is, however, fraught with challenges such as data fragmentation, privacy, algorithmic biases, and infrastructure. This review discusses the uses of AI and metaheuristic optimization for sustainable public health, the most impactful innovations, the challenges, and the constraints and with directions for future development. Overcoming the challenges and incorporating AI into current healthcare systems can make AI facilitate a more sustainable, personalized, and more equitable global healthcare system.