KUSB MBA Program aims to select the top 100 students while maintaining a minimum enrollment of 100 students annually. Our team developed a regression model using data to predict first-year MBA GPA, balancing student selection with enrollment needs. We focused on factors like GMAT scores, Undergrad GPA, and others. Analysis revealed strong correlations, with GMAT scores being the most significant predictor, explaining 57.6% of GPA variation. Multiple linear regression identified GMAT scores, Undergrad GPA, and international student status as key predictors, explaining 64.5% of GPA variation without signs of overfitting or multicollinearity. Predicted GPAs ranged around 3.1759, with a confidence level of 95% and a standard deviation of approximately 0.3206. Proposed cutoff GPA values were determined to maintain target admission numbers while considering enrollment yield, with a suggested cutoff of 3.1000 for a yield of 80%. Our model's positive predictive value (PPV) was approximately 0.9149, and the negative predictive value (NPV) was approximately 0.625, showcasing its reliability. However, the model had limitations, with 16 admitted students having GPA >3.2 and 81 rejected students having GPA <3.2 over the past two years. In conclusion, our model offers a robust approach to predict first-year MBA GPA, aiding in student selection while meeting enrollment goals. Using our model, KUSB GPA = 0.905+0.0028222*GMAT+0.21120112*UGGPA+0.06118586*INT we determined that maintaining a