Rapid and reliable tools are essential for urban regeneration and disaster risk mitigation in earthquake-prone regions. This study proposes a hybrid decision-support framework that integrates linguistic expert assessments with a probabilistic neural network (PNN) for rapid prioritization of urban building stocks. Eleven seismic vulnerability parameters were evaluated using a five-level linguistic scale and applied to 194 buildings affected by the 2023 Kahramanmara & scedil; earthquake in T & uuml;rkiye. The model achieved 100% training accuracy and 95% testing accuracy, with misclassifications limited to adjacent damage classes. The proposed framework can support municipalities in rapidly identifying high-risk buildings during urban regeneration planning. PCA-based visualization confirmed clear class separation and consistent generalization. A post hoc sensitivity analysis further quantified decision-boundary uncertainty, showing that near-boundary transitions were concentrated in a small number of influential parameters. Class-level stability indices indicated higher robustness for severe-damage predictions. Overall, the proposed framework offers a practical and operationally efficient tool for first-level seismic vulnerability screening and evidence-based urban regeneration prioritization.