<p>Understanding the dynamics of malaria prevalence is critical in malaria epidemiological surveillance, control strategies and treatment interventions. The study evaluated the socioeconomic determinants and predictive modelling of malaria prevalence using advanced regression and ensemble learning techniques. Multiple models (Linear regression, Ridge regression, Random Forest and Stacked ensemble approach) were developed using the Nigerian Demographic and Health Survey Program (NDHS) data containing environmental and demographic indicators. The Random Forest and Stacked models demonstrated superior predictive performance with an R² of 0.9826, 0.9815, and the lowest MSE of 0.00024, 0.00025 respectively. The key predictors such as population density (QR = 0.0072, ρ &lt; 0.001) malaria incidence (QR = 0.1043, ρ &lt; 0.001), vegetation index (QR = 0.0162, ρ &lt; 0.001, aridity (QR = 0.0168, ρ = 0.002) significantly increase malaria prevalence with R² of 0.9826. Temperature in January (QR = -0.0134, ρ &lt; 0.001) and potential evapotranspiration (QR = -0.0237, ρ = 0.004) were associated with reduced prevalence. Across age and gender groups, a higher burden among children under five and a slight male bias were observed with non-significant group-level differences (ρ &lt; 0.05), suggesting demographic vulnerabilities that warrant targeted intervention. A combination of statistical inference and machine learning demonstrated the role of environmental, demographic, and spatial factors in malaria transmission and provides a high-performing predictive tool for guiding surveillance and control strategies. </p>