Accurate mineral resource estimation and uncertainty assessment are essential for effective mine planning. This study integrates Ordinary Kriging (OK) and Sequential Gaussian Simulation (SGS) within a GIS-based geostatistical framework to estimate limestone resources and quality spatial uncertainty in the Qena limestone deposit, Egypt. A dataset of 68 exploration boreholes was analyzed to model limestone thickness and chemical constituents, with particular emphasis on CaO. Experimental semivariograms were developed, validated through cross-validation, and used for spatial estimation and simulation. OK was employed to generate prediction maps, while 100 SGS realizations were produced to assess spatial uncertainty and generate E-type and probability maps. The OK model estimated an ore area of 7.15 km², a volume of 31.71 million m³, and a limestone resource of approximately 79.27 Mt, with an average predicted CaO grade of 35.01%. The Gaussian model provided the best fit for limestone thickness, whereas the Exponential model provided the best fit for CaO, Al₂O₃, SiO₂, and MgO. The SGS E-type estimate produced a mean CaO value of 36.90%, compared with 36.03% for the original dataset and 34.99% for OK. Probability mapping at a 38% CaO cut-off identified zones with different levels of confidence for high-grade limestone occurrence, while the standard-deviation and grade–tonnage analyses provided additional information on spatial uncertainty and resource variability.The results indicate that OK provides reliable deterministic resource estimates, whereas SGS better represents geological variability and spatial uncertainty, supporting mineral resource evaluation and mine planning.