Precision agriculture relies heavily on information concerning assessing and mapping the spatial variation of soil attributes to ensure soil and plant sustainability. The current study was undertaken in the Gharb El-Mawhoub area of Dakhla Oasis to determine, predict, map, and assess the spatial variation of physicochemical attributes. Thirty-four geo-referenced soil profiles yielded a sum of 131 representative samples. Soil physicochemical properties, i.e., electrical conductivity (ECe), texture, sand, silt, clay, calcium carbonate, organic matter saturation percentage (SP), pH, cation exchange capacity (CEC), organic matter (OM), exchangeable sodium percentage (ESP), gypsum, and sodium absorption ratio (SAR) were measured. Following data normalization, classical and geostatistical approaches have been performed to characterize soil parameters and their spatial distribution. Semi-variogram models were used to quantify the spatial variation of physicochemical properties, and the ordinary kriging technique was applied to generate the respective maps. Accuracy of the prediction performance of models was assessed employing the cross-validation technique. Results showed that soil characteristics differed considerably throughout the area under study, with significant positive or negative correlation coefficients (P < 0.01 and/or P < 0.05). Furthermore, Rational Quadratic, Circular, Hole Effect, Pentaspherical, Exponential, Tetrasperical, and J-Bessel semi-variogram models were chosen as the best-fitted models for the investigated soil properties. Cross-validation results indicated that the selected models are the best-suitable semi-variogram models for estimation and mapping the spatial pattern surfaces of the soil attributes studied. The generated prediction maps provide valuable information concerning precision agriculture for improved soil productivity and limitation reduction. Therefore, these predicted maps have a high potential for application in site-specific management. Overall, the findings demonstrated that geostatistics approaches are powerful techniques for determining, predicting, and mapping the spatial interrelationships of soil attributes.