<p>This research documents a GIS-based multi-criteria methodology for land suitability evaluation of rice, wheat, and maize crops in Bilaspur district of Chhattisgarh, India, utilizing the Analytical Hierarchy Process (AHP)-integrated remote sensing and geospatial analysis. Ten agro-environmental factors-elevation, slope, rainfall, temperature, soil type, soil organic carbon (SOC), normalized difference moisture index (NDMI), sufficiency index, drain density, and distance to water bodies-were considered for the model. This research assessed the ten agro-environmental factors-elevation, slope, rainfall, temperature, soil, soil organic carbon (SOC), normalized difference moisture index (NDMI), sufficiency index, drainage density, and water bodies distance-which were used as the foundation for model building. Weights to the criteria were calculated through expert-informed pairwise comparisons and utilized in a weighted overlay analysis through ArcGIS to generate crop-specific suitability maps. The results revealed that rice suitability is determined by precipitation (weight 0.182), SOC (0.158), and proximity to water (0.132); while wheat and maize are sensitive to SOC (0.177 and 0.183, respectively), soil texture, and drainage. The spatial analysis reveals that the area is very high in suitability for rice is at 15.7%, while that for wheat is 14.18%, and for maize, it is 19.32%. However, over 50% of the area comes under marginal or unsuitable classes due to its limitations in terms of fertility and moisture. Bassett’s thesis has produced a finding of sensitivity analysis confirming the robustness of the model claim, such that it allows rice to be more sensitive to hydrological factors than wheat and maize, which are more ecologically adaptable. The combined AHP-GIS methodology will thus be a scalable instrument for precision agriculture regarding strategic land use planning and sustainable crop management.</p>