Infectious diseases remain a major public health and socio-economic challenge in the globe especially in Africa where issues such as environmental pollution, disease transmission, unequal healthcare access and resource limitations contribute to persistent disease vulnerability. The traditional public health approaches used in tackling infectious disease usually follows the generalized interventions which often overlook demographic, environmental, and socio-economic differences that causes disease risk across populations. Due to this, the use of precision public health (PPH) alongside artificial intelligence (AI) creates an opportunity that ensures that community at high risk are identified and prioritized during interventions. This review paper critically examines how AI contributes to PPH for infectious disease control and resource optimization in Africa through a systematic synthesis of literature published between 2005 and 2026. The Findings shows that machine learning, deep learning, and geospatial analytics can integrate epidemiological, climate, genomic, mobility, and social determinants data to support population risk stratification, targeted vaccination, precision vector control, and optimized healthcare resource allocation. These integrations have the potential to improve intervention efficiency, strengthen health equity, and maximize the impact of limited public health resources. However, translating these into a routine practice in Africa is quite challenging because of issues like fragmented data ecosystems, inadequate digital infrastructure, algorithmic bias, and weak governance policies. The study therefore concludes that while AI-enabled PPH offers significant potential to transform infectious disease management in Africa, its successful implementation will require sustained investment in key areas such as digital health infrastructure, workforce, and transparent governance systems.
