Moving beyond conventional mean-based approaches, this study examines the distributional and spatial heterogeneity of multidimensional poverty among rural households in Nigeria using quantile and spatial regression techniques. Data from the 2023/2024 Nigeria General Household Survey were used to construct a Multidimensional Poverty Index (MPI) for 2,602 households following the Alkire–Foster methodology, capturing deprivations in health, education, and living standards. Results show significant variation in poverty drivers across the deprivation distribution, with education consistently reducing poverty intensity, while household size and age display non-linear effects. Spatial analysis reveals lower multidimensional poverty in southern regions relative to the North Central and Northeast zones. Sensitivity tests confirm the robustness of findings. The study contributes methodologically by integrating quantile regression into multidimensional poverty analysis and offers policy-relevant insights for targeted, distribution-sensitive poverty reduction aligned with the Sustainable Development Goals.