Malaria remains a significant public health challenge in Nigeria, where climatic conditions favor transmission. Despite national declines, regional disparities persist, reflecting spatial and environmental heterogeneity. This study leverages a probabilistic learning framework, hierarchical Bayesian spatial modelling, to predict malaria prevalence among children aged 2–10 years and generate climate-sensitive risk maps for six southwestern states. Using malaria prevalence survey data from the Nigeria Malaria Indicator Surveys (NMIS) and climatic covariates from the Demographic Health Survey (DHS) spatial repository, we implemented the model within the Integrated Nested Laplace Approximation (INLA) framework, incorporating structured spatial effects via an intrinsic conditional autoregressive prior (ICAR) and unstructured random effects to capture non-spatial variability. Results reveal significant spatial heterogeneity, with Osun recording the highest prevalence (47%), followed by Oyo (45%), Ekiti (44%), Ondo (38%), Ogun (31%), and Lagos (12%). Climatic factors had a marginal influence, with aridity inversely related to prevalence, temperature positively associated, and rainfall exhibiting a non-linear effect. The results indicate that while climate plays a role, local environmental and socioeconomic determinants may also influence malaria prevalence. By integrating spatial dependencies and uncertainty quantification, this approach demonstrates how Bayesian learning can support predictive analytics and data-driven malaria intervention strategies, bridging statistical modelling and machine learning for public health policy.