Groundwater resources are vital for human and environmental needs, especially in humid and semi-arid regions. Conventional groundwater quality models, including statistical and single-algorithm machine learning techniques, often lack accuracy, interpretability, and scalability. This study presents an advanced ensemble machine learning framework for assessing groundwater quality in Ethiopia's Upper Awash River Basin, Africa. The Entropy Weighted Water Quality Index (EWQI) consolidates 13 hydrochemical parameters, including electrical conductivity, total dissolved solids, pH, and major ions. Data preprocessing involved imputation, standardization, and partitioning into training sets (70 %) and testing sets (30 %). Predictors include elevation, slope, land cover, lithology, and soil characteristics (type, moisture, and temperature). A novel stacking ensemble model was developed using Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbors, and EXtreme Gradient Boosting. The stacking model outperformed individual models, achieving training metrics of MSE 17.96, RMSE 4.24, and R2 0.97, as well as testing metrics of MSE 76.29, RMSE 8.73, and R2 0.87. The validation results showed an MSE of 67.18, an RMSE of 8.2, and an R2 of 0.89. Beyond accuracy, SHAP interpretation shows that soil temperature, land cover, and soil moisture are the dominant drivers of EWQI, exceeding terrain and lithologic controls. By coupling an objective EWQI target with broadly available covariates and an interpretable stacked ensemble, the study links prediction to actionable land and water management in a datascarce basin and outlines a transferable workflow.