<p>This study investigates the use of Artificial Intelligence (AI) and geospatial techniques in generating soil fertility maps for precision agriculture, with a focus on comparing Ordinary Kriging (OK) and Support Vector Machine (SVM) interpolation methods. The study was conducted on soil samples collected from the Bundelkhand region in Uttar Pradesh, India, covering 66 geo-referenced points at a depth of 0–20&#xa0;cm. Key soil properties such as pH, EC, OC, P, K, and S were analysed. The SVM, OK interpolation method and RF-OK hybrid model were applied using GIS software to predict unsampled areas’ fertility values. Model evaluation was based on R² and RMSE metrics, and the efficacy of each technique was validated through cross-validation. The study generated digital soil maps for SVM and Kriging methods, visually comparing outputs for each soil parameter to determine the optimal mapping technique for precision agriculture. The results of RF-OK hybrid model outperformed from both remaining techniques with higher R² values and lower RMSE values for all the soil attributes. In comparison of OK and SVM, OK interpolation displayed higher accuracy for pH, EC, OC, and K parameters as comparison to SVM while SVM performed better for P and S, as indicated by lower RMSE and higher R² values for the respective parameters as compared to Kriging. Specifically, OK achieved superior R² for pH (0.685) and EC (0.382), while SVM outperformed in P with an R² of 0.257. The spatial variability analysis revealed strong spatial dependence for pH, EC, and OC, suggesting high reliability of OK in spatial data representation.</p>