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Acidic soils (pH < 5.5) cover roughly 30% of Sub-Saharan Africa. Low nitrogen fertilizer application (15 kg N ha⁻¹ yr⁻¹) has no effect on soil acidification in Sub-Saharan Africa (SSA). However, the effect of optimized fertilizer use on soil acidification (H⁺) in SSA crops remains unknown. This study intended to predict the spatial variation of H⁺ caused by optimized fertilizer use using data from 5782 field trials in SSA cropland. We used ensemble machine learning to predict spatial variation (H⁺) after measuring the inputs and outputs of major elements and their effect on H⁺ production. The results revealed that H⁺ ranged spatially from 0 to 16 keq H⁺ ha⁻¹ yr⁻¹. The most protons (H⁺) were produced by cassava, banana, and Irish potatoes systems with 12.0, 9.8, and 8.9 keq H⁺ ha⁻¹ yr⁻¹, respectively. The results of the 10-fold cross validation for the soil acidification model were a coefficient of determination (R²) of 0.6, a root mean square error (RMSE) of 2.1, and a mean absolute error (MAE) of 1.4. Net basic cation loss drives soil acidification under optimized fertilizer application and climate covariates had a higher relative importance than other covariates. Digital soil mapping can produce soil acidification maps for sustainable land use and management plans.