Reliable forecasting of groundwater levels (GWL) is essential for effective groundwater resource planning and management. This study presents a multi-step-ahead GWL prediction model that harnesses ensemble learning and optimization strategies to support proactive water resource management. The research was conducted using 65533 instances of data from well W0000382–1 in Markham, Ontario, characterized by a well diameter of 5.08 cm, depth of 21.3 m (below the ground), and screen interval of 17.4–20.4 m in a sandy, silty aquifer. The multi-step ahead time-series data were structured using five lagged input steps (t-1 to t-5) to predict the current GWL (t), with dimensionality reduced via Principal Component Analysis (PCA). PCA results confirmed that the first component alone explained 99.85% of the total variance, validating the use of past GWL observations as strong predictors. Four models were evaluated on test data: Bagged Tree Ensemble (BaT-E), Boosted Tree Ensemble (BoT-E), Bayesian Optimization (BO-E), and Stepwise Regression (SWR). Among them, BaT-E exhibited the best performance, with a test MAE of 0.00554, an RMSE of 0.00771, a prediction speed of 60,000 observations per second, and a training time of 53.63 s. SWR followed with a MAE of 0.00539, RMSE of 0.00761, and superior computational efficiency (750,000 obs/s prediction speed; 9.17 s training time). This analysis establishes BaT-E as the most reliable model for precise GWL forecasting, striking an effective balance between accuracy and computational efficiency. Such predictive frameworks are essential for advancing the SDGs, supporting the mandates of the CCME and the U.S. EPA’s strategies for sustainable aquifer and groundwater system management.