Information on the spatial distribution of soil pH is essential for assessing soil quality and soil productivity. Digital soil mapping (DSM) is commonly used to predict soil characteristics over various types of landscapes. Over the past decade, researchers have made progress using machine learning techniques to provide reliable predictions of soil properties with limited data. DSM studies often use a single learning approach, which is constructed with a machine learner that systematically extracts soil–environment relationships from a large database, whereby a fitted model is used to predict soil information in an unmapped area. The practice of using an ensemble learning approach, especially one that combines several base learners, has rarely been tested in DSM. We developed a workflow for using an ensemble learning algorithm to predict soil properties for the Thompson-Okanagan region of British Columbia, Canada. Here, we focused on soil pH and tested a variety of base learners. Base learners with high prediction accuracies were then used to construct a SuperLearner (SL) to extract the complex relationships between soil properties and environmental variables. The fitted SL was then used to predict soil properties at 25 m spatial resolution at three depth intervals (0–5, 5–15, and 15–30 cm). Prediction accuracies were assessed using an independent test dataset, which indicated that the SL had a similar prediction accuracy to the best individual base learners. Using the heterogeneous ensemble learning approach with a weighted average stacked generalization process eliminated the need to choose the best base learner.