Kaolin is a strategically important industrial mineral widely used in the ceramics, paper, rubber, and refractory industries. However, accurately estimating kaolin resources is often challenging due to the scattered distribution of borehole data and the complex geological heterogeneity of deposits. Reliable resource estimation is essential for optimizing extraction processes and promoting sustainable mineral development. This study integrates Geographic Information Systems (GIS) and geostatistical techniques to assess the spatial distribution and estimate the resource potential of kaolin deposits in the Wadi Kalabsha region of southwestern Aswan, Egypt. A dataset comprising 71 boreholes was analyzed using ArcMap for spatial interpolation and zonal statistics. Ordinary Kriging (OK) was applied to predict ore thickness, while Indicator Kriging (IK) was employed to model the non-normally distributed Al₂O₃ concentrations. Experimental variograms were constructed and fitted with theoretical models, with the Gaussian model providing the best fit based on cross-validation metrics (RMSE, ME, RMSSE). Kriging results were used to generate a 3D block model in Surpac (block size: 12.5 m × 12.5 m × 1 m), facilitating accurate estimation of both ore volume and grade. The total estimated resource was approximately 11.34 million tonnes (5.67 million m³), with an average Al₂O₃ grade of 32.2%. Grade-tonnage curve analysis demonstrated the trade-off between ore quality and quantity across different cut-off thresholds. These cut-off thresholds are consistent with industrial requirements for ceramic and pharmaceutical applications, providing a preliminary indication of the economic usability of the estimated kaolin resource. The study underscores the effectiveness of integrating GIS and geostatistics for robust resource estimation and offers a transferable framework for evaluating industrial mineral deposits in geologically complex and data-limited regions.