Ohize, HOUmaru, ETOnumanyi, Adeiza JChingle, MPFolorunso, SOAmbafi, JGYusuf, IEneojo, AEOhize, SO2026-03-102026-03-102025-122731-0809https://doi.org/10.1007/s44163-025-00739-2http://hdl.handle.net/10204/14738In recent years, artificial intelligence (AI) has gained recognition as a transformative tool, aiding in the prediction of disease patterns, outbreak control, and the efficient distribution of medical resources. This review explores the extensive contributions of AI in epidemic response, with particular emphasis on its application to Lassa fever. The review begins by analyzing the disease’s epidemiology and transmission patterns, laying the groundwork for understanding AI-driven approaches. Key AI technologies such as machine learning, deep learning, and natural language processing are examined for their impact on surveillance, diagnostics, and treatment innovation. Successful implementations include predictive models for outbreak identification and enhanced vaccine research. However, the integration of AI in epidemic contexts continues to face challenges, including insufficient epidemiological data, high computational requirements, and difficulty incorporating AI within existing healthcare infrastructures. These issues are particularly pronounced in the management of Lassa fever, where data limitations and disease variability add layers of complexity. Existing reviews fail to adequately address the latest AI advances in this domain, particularly in relation to implementation challenges, global trends, and emerging concerns. This gap is addressed by offering a comprehensive overview of AI-driven techniques, ongoing developments, and practical solutions tailored to Lassa fever control and prevention. Ultimately, this review champions an inclusive AI framework that improves preparedness and adaptability in the response to epidemics. By extrapolating insights from Lassa fever, it provides a strategic guide for stakeholders, scientists, healthcare professionals, and policymakers, seeking to take advantage of AI to strengthen public health resilience and epidemic management.FulltextenArtificial IntelligenceAILassaMachine LearningMLDeep LearningNLPPublic HealthArticleN/A