Background <p>The persistent conflict between Fulani pastoralists and agrarian communities in Nigeria’s Benue Valley constitutes a significant security and socio-economic challenge, primarily driven by competition over diminishing land and water resources and compounded by religio-cultural tensions. While transitioning from open grazing to ranching is frequently proposed as a sustainable solution, its adoption is severely limited by the scarcity of hydrologically suitable sites. This study addresses this constraint by delineating groundwater potential zones to guide scientifically informed siting of ranches in Benue State. The study specifically advances beyond pure resource assessment to actionable conflict-resolution planning.</p> Methods <p>An integrated methodological framework was adopted. Ninety-six Vertical Electrical Sounding data were acquired, with resistivity data interpreted through partial curve matching and one-dimensional inversion using WinResist®, achieving a root-mean-square error below 5%. Key aquifer parameters—including resistivity, thickness, hydraulic conductivity, transmissivity, and longitudinal conductance—were computed to characterize the aquifer. Lineament features were extracted from Landsat 8 imagery and aeromagnetic data using the Canny Edge Detection algorithm and lineament density calculated. Lithology of the study area was obtained from geologic map. Seven thematic factors controlling groundwater occurrence were weighted via the Analytical Hierarchy Process and integrated through a weighted overlay analysis in ArcGIS 10.8 to generate a Groundwater Potential Map. Model performance was validated against borehole yield data and further assessed through sensitivity analysis with ±20% variations in factor weights.</p> Results <p>Results delineated three groundwater potential zones—low, moderate, and high. The high groundwater potential zone is predominantly associated with the Katsina-Ala River floodplains, exhibiting dense lineaments, permeable lithologies, and significant aquifer thickness and covers approximately 417 km². Model validation indicated 84.61% predictive accuracy, while sensitivity analysis highlighted lineament density as the most influential factor, causing ±5.62% variation in model outputs.</p> Conclusion <p>The study demonstrates the effectiveness of integrating geophysical, remote sensing, and geospatial techniques for groundwater resource identification. The findings provide a scientifically robust and policy-relevant framework for mitigating farmer-herder conflicts via evidence-based land-use planning. Successful implementation in high-potential zones will require prudent groundwater management to prevent contamination and parallel engagement with the complex socio-political dimensions of land tenure.</p>