Identification and ranking of road accident hotspots play a crucial role in enhancing road safety and reducing human casualties. In this study, a hybrid decision-making model based on Data Envelopment Analysis (DEA) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which manages uncertainty in data using Evidence Theory. In the first step, the efficiency of each road segment was calculated through DEA, and based on these results, the initial ranking was performed using TOPSIS. Subsequently, Evidence Theory was applied to integrate the outcomes and provide a final, reliable ranking. The dataset consists of ten simulated accident-prone points characterized by ten quantitative and qualitative performance indicators, which can be replaced with real data. The quantitative results show that point P7, with a safety score of 0.9553, is the safest, while point P9, with a score of 0.3975, is identified as the most hazardous location. The novelty of this research lies in integrating DEA, TOPSIS, and Evidence Theory within a unified framework to handle uncertainty and achieve stable ranking results. This model can serve as an effective decision-support tool for strategic road safety management.