Soil fertility is pivotal for agricultural productivity, and iron (Fe) is a critical micronutrient essential for a successful crop development. This study investigates a potential of ensemble machine-learning methods in geospatial prediction of soil Fe in Croatia. Using a dataset of 686 soil samples, three individual machine-learning methods, including the extreme gradient boosting (XGB), support vector machine (SVM), and Cubist, as well as their ensemble, were evaluated for the soil Fe prediction. The ensemble method outperformed the individual models, exhibiting a higher prediction accuracy expressed by the coefficient of determination (R2 = 0.578), with a lower root-mean-square error (RMSE = 0.837) and the mean absolute error (MAE = 0.550). The soil clay content emerged as the most influential predictor, followed by the sand content, pH values, and select bioclimatic variables. This study’s results demonstrate the effectiveness of ensemble machine learning in an accurate prediction of soil Fe content and contribute to an informed decision-making in sustainable agricultural land-use planning and management. By including the complementary machine-learning methods into an ensemble with the representative environmental covariates, a geospatial prediction aids to a reliable comprehension of soil properties and their spatial variability