Geopolymer concrete (GPC) serves as an environmentally conscious alternative to traditional concrete, offering a sustainable solution for construction needs. The ability to make on-site changes is dependent on the concrete’s strength after casting, which must be higher than the target value. To anticipate the concrete’s strength before it is poured is, thus, quite helpful. Three ensemble machine learning (ML) approaches, including gradient boosting, AdaBoost regressor, and extreme gradient boosting, are presented in this work as potential methods for forecasting GPC’s mechanical strength that incorporates corncob ash. To determine which modeling parameters are crucial, sensitivity analysis was employed. When the compressive strength and split-tensile strength of GPC were tested with ensemble ML models, R ² values of more than 90% were discovered between the predicted and actual results. Statistics and a k-fold analysis based on the error and coefficient of determination were used to verify the developed models. Slag amount, curing age, and fine aggregate quantity were the three mix proportions that had the most impact on GPC’s mechanical strength, as shown in the sensitivity analysis. The results of this study demonstrated that ensemble boosting approaches could reliably estimate GPC mechanical strength. Incorporating such procedures into GPC quality control can yield significant improvements.