Digital Soil Mapping (DSM), as a modern approach to soil map production, offers higher accuracy and efficiency compared to traditional methods by utilizing advanced algorithms and diverse environmental data. This study aims to compare the performance of several machine learning models—including Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Discriminant Analysis (DA), Multiple Logistic Regression (MLR), Decision Tree Analysis (DTA), and Random Forest (RF)—in spatial prediction of soil classification classes, from the order to family level, within the DSM framework. The input data were derived from satellite imagery and environmental covariates related to the Badr watershed, located in Kurdistan Province, Iran. Additionally, an ensemble logistic regression model was employed to integrate the outputs of the aforementioned models. Results of 10-fold spatial cross-validation showed that the ensemble logistic regression model achieved the highest accuracy in predicting most soil classification levels. The Random Forest model consistently ranked second in prediction performance, while KNN showed the lowest accuracy. Among the environmental covariates, geomorphology, topography, and vegetation cover were identified as the most influential factors in predicting soil classes from order to subgroup levels. However, vegetation cover was less significant at the family level. The findings of this research highlight the potential of machine learning models to enhance the accuracy of soil maps and contribute to sustainable natural resource management.