<![CDATA[Although the Fuzzy Analytic Hierarchy Process (F-AHP) is widely used to address complex multi-criteria decision-making problems under uncertainty, its application to malaria intervention prioritization in resource-constrained settings, particularly North-East Nigeria, remains limited. This study developed and applied an F-AHP decision-support framework to prioritize malaria control interventions based on epidemiological, economic, operational, and social considerations. A mixed-methods approach involved 18 purposively selected experts, including epidemiologists, medical doctors, and health educators from the six states of North-East Nigeria. Primary data were collected through structured questionnaires, while secondary evidence was drawn from peer-reviewed literature, national malaria control guidelines, and relevant health reports. Five evaluation criteria and five intervention alternatives were assessed using fuzzy pairwise comparison matrices. Expert judgments were represented by triangular fuzzy numbers and defuzzified using the centroid method to generate normalized priority weights and global intervention rankings. All judgment matrices met the recommended consistency threshold (CR ≤ 0.10), supporting the internal consistency of the evaluations. Acceptability emerged as the most influential criterion (0.387), followed by cost-effectiveness (0.283) and health impact (0.200). Among the intervention alternatives, insecticide-treated nets/long-lasting insecticidal nets ranked first, with a global priority weight of 0.346, followed by indoor residual spraying at 0.278. These findings demonstrate that social acceptability, economic efficiency, and health impact are central determinants of malaria intervention priorities in the region. The study contributes to fuzzy multi-criteria decision-making in public health by providing a transparent and systematic framework for incorporating uncertainty into expert judgments. Practically, the framework can support policymakers, malaria control programmes, and public health stakeholders in allocating limited resources and selecting contextually appropriate interventions in North-East Nigeria. It also provides a foundation for integrating stochastic and adaptive approaches into future malaria intervention prioritization under temporal and epidemiological uncertainty.]]>
