Traditional statistical methods have faced serious challenges due to the expansion of spatial data with complex spatiotemporal structure. These data require specialized methods due to spatial autocorrelation, variance heterogeneity, and complex geographical dependencies. In this study, support vector regression is introduced as a novel approach for analyzing and modeling the complex spatial structure of geostatistical data related to soil calcium and magnesium contents. This analysis is performed based on different geographical coordinates (east–west and north–south), at two depths of 0–20 cm and 20–40 cm, and across three distinct geographical regions. The support vector regression method, with its capability to model complex nonlinear relationships while preserving the spatial structure of the data, allows for more accurate and realistic prediction of the nutrient elements' distribution in soil. This approach, utilizing kernel functions, enables the analysis of high-dimensional feature spaces and structural complexities, proving its effectiveness against noise and outliers. To accurately measure the efficiency of support vector regression, its performance is compared against ridge regression.