This study aimed to develop a transparent district-level Maternal and Child Health (MCH) index for Uttar Pradesh (UP), India, using a hybrid methodological framework integrating Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Geographic Information Systems (GIS), to support spatially targeted and equitable health planning.

MCH is a key indicator of equity and effectiveness within health systems, directly impacting the wellbeing of mothers and children. Despite global efforts, many low-and middle-income countries continue to face preventable maternal deaths and child illnesses. In Uttar Pradesh (UP), substantial inter–district disparities in MCH outcomes persist but are often masked by state-level averages, hindering targeted policy and resource allocation.

We applied a hybrid Multi-Criteria Decision-Making (MCDM) framework. AHP was used to assign weights to nine key MCH indicators covering antenatal care, skilled birth attendance, child immunization, and nutrition based on National Family Health Survey (NFHS-5, 2019–21) data across 75 districts. TOPSIS was then employed to rank districts by overall MCH performance. GIS was used to visualize spatial disparities and identify clusters of high and low performance.

The MCH Index revealed substantial spatial disparities across UP. Districts such as Barabanki, Mahamaya Nagar, and Unnao ranked highest, while eastern UP and Bundelkhand showed lower performance. AHP assigned the highest importance to skilled birth attendance (22%) and antenatal care visits (22%). TOPSIS rankings highlighted gaps in maternal health services in socioeconomically marginalized districts. GIS mapping identified clusters of vulnerability linked to infrastructure and poverty. The AHP-TOPSIS-GIS framework provides a replicable method for sub-state MCH assessment, enabling policymakers to prioritize underserved districts and reduce geographic health outcomes. The findings underscore the need for decentralized, equity-focused strategies tailored to local contexts. Future research should incorporate temporal changes and socio-environmental factors to strengthen planning and monitoring.