This study examines the role of regression analysis in evaluating supplier performance and procurement outcomes, aiming to enhance data-driven decision-making in procurement management. Using a quantitative research design, multiple regression models were applied to procurement data from 2020 to 2024 to assess relationships between key variables such as supplier reliability, cost efficiency, delivery time, and procurement success. The findings indicate a significant positive correlation (r = 0.85, p < 0.001) between supplier reliability and procurement outcomes, demonstrating that higher supplier reliability leads to improved procurement efficiency and a 50% reduction in procurement costs. A chi-square test confirmed that procurement risks align closely with predictive models (χ² = 3.56, p = 0.46), while a t-test showed a 16.7% decrease in procurement risk after implementing regression-driven policies (t = 3.27, p = 0.002). These results validate the effectiveness of regression analysis in supplier evaluation, risk prediction, and procurement cost optimization. The study recommends enhanced data management, advanced training in statistical analysis, adoption of predictive modeling, promotion of a data-driven procurement culture, and integration of regression analytics into procurement software to improve decision-making and efficiency.