Application of Artificial Intelligence in the Management of Climate Change-Related Health Issues in Africa: A Review

Abstract Background Climate change disproportionately threatens public health in Africa, accelerating vector-borne outbreaks and resource scarcity. While Artificial Intelligence (AI) offers predictive tools to mitigate these threats globally, its application within African public health frameworks remains under-documented. This study maps the thematic divergence between global and African research landscapes regarding AI applications in climate-health management. Methods Using a Scoping Review framework guided by Synthetic Knowledge Synthesis (SKS), we queried the Scopus database for publications (up to October 2025) integrating AI, climate change, and health. Bibliometric mapping and thematic clustering were executed using VOSviewer to compare the Global Literature Corpus (GLC) and the African Literature Corpus (ALC). Results Global research emphasizes complex environmental modeling, air pollution mortality tracking, and the integration of personal exposomes with chronic disease outcomes. Conversely, African research focuses heavily on localized, reactive solutions—primarily supervised machine learning for infectious disease triage (HIV, malaria) and food security. Significant barriers, such as a localized "compute deficit," data silos, and energy instability, prevent the scaling of advanced AI climate-health models in Africa. Conclusions A significant research and implementation gap exists between African and global AI-driven climate-health initiatives. To build public health resilience, international funding must prioritize localized, predictive AI frameworks and address foundational infrastructure deficits across the continent.