Musuka, GodfreyUmar, Al-umraDadari, IbrahimMoyo, EnosMano, OscarIradukunda, Patrick GadMbunge, ElliotMurewanhema, GrantDhliwayo, TapiwaMataruse, NoahSayem, Abu Sadat MohammadDzinamarira, Tafadzwa2026-03-252026-03-252026-04Musuka, G.., Umar, A., Dadari, I. et al. 2026, 'Bridging immunization gaps in Sub-Saharan Africa: a narrative review of microplanning, geospatial, and machine learning approaches to reach zero-dose children and under-immunised children', Vaccine, vol. 79, art. 128413, pp. 1-11, doi : 10.1016/j.vaccine.2026.128413.0264-410X (print)1873-2518 (online)10.1016/j.vaccine.2026.128413http://hdl.handle.net/2263/109291DATA AVAILABILITY : No data was used for the research described in the article.Immunization inequities persist across Sub-Saharan Africa, with significant numbers of zero-dose and under-immunised children contributing to preventable morbidity and mortality. This narrative review critically examines the integration and effectiveness of machine learning, geospatial mapping, and microplanning strategies in identifying and reaching these vulnerable populations. The review's primary objective is to synthesise current evidence on how these innovative approaches are being applied within routine immunization systems to address persistent coverage gaps. A systematic search of peer-reviewed literature and grey sources was conducted, focusing on studies and programmatic reports from 2015 to 2025. The review analyses methodological trends, implementation experiences, and outcome data related to machine learning algorithms for risk profiling, geospatial technologies for mapping and targeting, and microplanning tools for local-level action. Data extraction and thematic synthesis were guided by the WHO framework for immunization equity. Findings demonstrate that machine learning models, utilizing demographic, health system, and mobility data, have enhanced the precision of zero-dose child identification, enabling more targeted outreach interventions. Geospatial mapping has further enabled real-time visualisation of immunization deserts and the spatial distribution of missed communities, supporting resource allocation and deployment of mobile teams. Microplanning, when integrated with digital tools and community engagement, has shown promise in translating high-level data into actionable local strategies, improving follow-up, and reducing missed vaccination opportunities. Despite these advancements, several challenges persist. Data quality and interoperability issues limit the scalability of machine learning and geospatial solutions, particularly in remote or fragile settings. Capacity gaps at the sub-national level, including technical skills and digital infrastructure, impede effective microplanning and data use. Furthermore, the sustainability of these approaches is threatened by fragmented investments and limited integration into national health information systems. Opportunities exist to strengthen the routine immunization system by standardising data collection, investing in workforce training, and fostering cross-sectoral collaboration. The review recommends prioritising the development of interoperable platforms, expanding context-specific pilot projects, and embedding evaluation mechanisms to track impact and equity outcomes. Policymakers are urged to leverage the demonstrated benefits of machine learning. HIGHLIGHTS • Immunization inequities persist across Sub-Saharan Africa. • We examined machine learning, geospatial mapping, and microplanning strategies in immunization programmes. • Machine learning models, geospatial and microplanning approaches have utility in zero-dose child identification. • Data quality and interoperability limit the scalability of these solutionsen© 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.Immunization gapsUnder-immunised childrenZero-doseMicroplanningGeospatialMachine learningNarrative reviewSub-Saharan Africa (SSA)Article