The determination of Safe Bearing Capacity (SBC) is critical in the design process as it directly affects the structural integrity and safety of a structure. Geostatistical techniques enabled the engineers to model and predict soil behavior at unsampled locations based on data collected from the nearby points. The main objective of this study is to test the applicability of geostatistical methods and machine learning tool on soil data to estimate the SBC of the soil. The study area is restricted to the Mysore district, Karnataka, India, and a total of 103 soil data sets are collected. The collected data points are interpolated using the Inverse Distance Weighted (IDW) method, Kriging, 10 cross-fold validation method, and Root Mean Square Error (RMSE) value. The IDW method is carried out using QGIS software. The interpolated layer is shown on the Mysore district map. Further, using the interpolated layer, contours are generated with an interval of 0.015 m for depth, and SBC contours are generated with an interval of 5 kN/m2. According to the results, the IDW approach is the most accurate for the provided data set among the three since it produced the least RMSE value. Since there are fewer data points and no specific location is available, accuracy is impacted, necessitating additional testing with an Artificial Neural Network tool. The findings of this research will contribute to enhancing the understanding of the potential benefits and limitations of geostatistical and machine learning techniques in geotechnical engineering, facilitating their practical implementation in real-world projects, and ultimately improving the overall performance and safety of geotechnical structures and infrastructure.