Accurate prediction in regression tasks is critical for data-driven decision-making across a wide range of domains. This study investigates the performance of several regression techniques—with the objective of identifying an optimal predictive model. To enhance accuracy and robustness, a Stacked Regressor ensemble model is also developed by combining the outputs of SVR, KNN, and Random Forest using Ridge Regression as the meta-learner. The models are evaluated using standard performance metrics based on the errors to estimate how well the model is working. Experimental results demonstrate that the Stacked Regressor consistently outperforms the individual base models, achieving the lowest error rates and the highest coefficient of determination (R2). While traditional models like Linear Regression offer interpretability and simplicity, they fall short in capturing complex, non-linear relationships. The ensemble approach of the Stacked Regressor leverages the diversity and strengths of its base learners, resulting in more accurate and generalizable predictions. This work highlights the effectiveness of ensemble learning techniques—particularly stacking—as a powerful tool in regression modelling and recommends their adoption in applications where predictive precision is essential specially in the problems related to education and they have provided the improvement of 10%.