This study examines using Simple Additive Weighting (SAW) in a higher education decision support system (DSS) for scholarship selection. Since scholarship grants must be transparent and equitable, the SAW method provides a structured way to evaluate candidates using many weighted criteria. Academic Performance, Financial Need, Extracurricular Activities, and Personal Statements are weighted to represent their importance to the scholarship’s goals. The study uses the SAW method to normalize applicant scores to a standard scale and apply pre-determined weights to create composite scores. The results show that the SAW technique ranks candidates by total weighted scores, giving a quantitative basis for scholarship decisions. The method’s simplicity and customizable flexibility promote fairness and clarity in selection. However, weight assignment biases and the method’s measurable data constraint are acknowledged. The study advocates using more advanced algorithms to handle qualitative assessments and suggests researching automated weight adjustment and hybrid decision-making models. This research sheds light on DSS in education and emphasizes the necessity for continuous development to connect decision-making with educational goals and fairness.