Africa’s vaccine and drug research and development capacity remains limited by infrastructural gaps, fragmented data systems, and shortages of skilled personnel, constraining timely therapeutic discovery and clinical translation in low-resource settings. Artificial intelligence (AI) and machine learning offer potential solutions by enabling predictive modelling, accelerating compound screening, improving genomic surveillance, and supporting adaptive clinical trial design. This narrative review synthesizes studies and institutional reports published between 2015 and 2025 from major scientific databases to examine AI applications in vaccine and drug development relevant to African contexts. Thematic analysis identified key patterns related to infrastructure readiness, workforce capacity, and translational implementation, with findings validated through evidence triangulation and consensus review. Results show that AI platforms have supported infectious disease candidate identification, pandemic vaccine development, malaria drug resistance mapping, and predictive analytics for vaccine distribution. While accelerated outcomes were evident during public health emergencies, routine implementation remains constrained by resource availability. Major challenges include inadequate digital infrastructure, fragmented regulatory systems, and limited technical expertise despite ongoing capacity-building initiatives. The review proposes an integrated framework linking infrastructure, skills development, and ethical governance as critical factors for sustainable AI adoption in African biomedical research. Strengthening investment, fostering regional collaboration, and developing context-specific ethical frameworks are essential to ensure equitable access, enhance innovation capacity, and build resilient biomedical research ecosystems across Africa.
Chandipwisa et al. (2026) studied this question.