Abstract The management of water resources is becoming a more pressing issue in semi-arid areas, such as Marathawada, where excessive groundwater use and erratic rainfall patterns have led to severe water scarcity. By utilizing geostatistical modeling approaches integrated into the QGIS platform, this study seeks to evaluate and forecast the spatial distribution of water resource availability in the Marathawada community. Sustainable resource planning and localized water governance are supported by this research's data-driven approach, which makes use of elevation models, hydrological inputs, and spatial data. Digital elevation models (DEMs), land use/land cover (LULC) data, rainfall records, and groundwater depth measurements were all included in the extensive dataset that was gathered and preprocessed using QGIS. The study utilized spatial interpolation techniques, specifically kriging and inverse distance weighting (IDW), to evaluate the potential for surface water and groundwater availability in different sub-regions. To guarantee model reliability, statistical accuracy metrics like RMSE and cross-validation were used to assess the choice of interpolation techniques. The generated maps of water availability show notable geographical heterogeneity driven by anthropogenic demand, land use patterns, and terrain. While low-lying areas with extensive agricultural activity revealed indicators of water stress, areas with lush vegetation and little urbanization showed increased groundwater recharge capacity. The significance of combining local environmental indicators with geostatistical modeling is highlighted by this spatial divergence. The study shows how useful QGIS is as a powerful open-source GIS tool for conducting predictive mapping and spatial analysis in areas with limited resources. It also emphasizes how QGIS-based technologies may improve watershed management plans, influence regional water policy, and direct future infrastructure design. This study highlights the importance of spatial intelligence in preventing future water scarcity in the Marathawada region and advances a more comprehensive understanding of the distribution of water resources by taking a geospatial approach.