Multicollinearity often complicates regression analysis, both in classical and fuzzy input setup. This research introduces a new approach that combines ridge regression with fuzzy regression to tackle correlated covariate’s impact, with a specific focus on improving education policy systems. Our method utilizes the \(\alpha \) -level estimation algorithm and a dataset where Grade Point Average (GPA) serves as a fuzzy input, while input variables remain crisp. We assess our estimator’s performance using RMSE and MAPE. This applied research showcases the potential of our method in enhancing education policies through more accurate data-driven decision-making.