This study uses the Simple Additive Weighting (SAW) method to examine the use of an AI-enhanced Decision Support System (DSS) to evaluate vocational school teachers. The research uses SAW in a DSS framework to assess classroom management, student engagement, and instructional efficacy. This emphasizes objective, thorough teacher assessments. Weighting these attributes based on their impact on educational achievements allows for more nuanced evaluations that reflect the institution’s values. Value normalization is the initial step in establishing uniformity across all criteria scales. The aggregated values are used to calculate each teacher’s performance score after weighted scores are calculated. The use of a clear and quantifiable method to compare teacher performance reduces subjective bias in educational evaluations. The Disability Support System (DSS) is designed to inform teacher development and recognition decisions based on statistics. However, the assumption of criteria independence and weight allocation sensitivity may affect evaluation fairness and accuracy, according to the research. Future research should combine the SAW technique with other decision-making models and investigate dynamic weight adjustments to improve the system’s responsiveness to changing educational aims. The DSS’s clear, objective, and reproducible teacher evaluation method could change educational administration. This method could improve education and teacher training.