<p>This research work employs advanced geostatistical and machine learning methods to analyze and model spatial data, with a focus on zinc concentration measurements. The primary objectives are to evaluate and compare these methods in terms of generating accurate spatial predictions, quantifying uncertainty, and identifying critical spatial patterns. The proposed approach uses self-organizing maps (SOM) to augment the applicability of ordinary kriging (OK) to a very large dataset. The classical OK approach and the Gaussian process regression (GPR) method were applied for comparison purposes, as they are widely used methods. All three methods are suitable for both fields explored herein, geostatistics and machine learning. In preliminary analysis, a variety of kernels were tested, including the novel harmonic covariance estimator (HCE). The exponential kernel was selected for the comparisons among the three methods. GPR is a flexible Bayesian approach that is exceptionally efficient in capturing complex spatial patterns and providing robust uncertainty estimates. While both GPR and OK aim to achieve accurate spatial predictions, SOMs enhance kriging by classifying data and facilitating predictions based on data from the best-matching neuron, effectively adapting kriging to localized spatial patterns and improving the interpretability of spatial dependencies. This integration of methodologies demonstrates the combination of stochastic geostatistical methods with machine learning for an improved understanding and prediction of spatial phenomena. The results highlight the enhanced management and evaluation of natural resources made possible by this modeling approach, specifically in the case of a zinciferous ore deposit. More specifically, the research findings indicate that the conventional selection of neighborhoods in OK tends to favor certain validation measures. In contrast, the SOM-guided selection of the neighborhood enhances the predictive–observational correlation.</p>