Ageing building stock, shrinking budgets, and inspector shortages hinder timely façade safety inspections. This research develops an automated damage detection and risk prioritization system for aging concrete structures. Five YOLOv11 variants were trained on 130,838 high-resolution images from 25 Seoul districts to detect three critical damage types: cracks, exposed rebar, and spalling. The proposed framework integrates YOLOv11 detection with a novel Damage Criticality Index (DCI) that transforms five visual-spatial cues—area, multiplicity, confidence, density, and spread—into continuous severity scores, subsequently categorized into low, medium, and high risk via K-means clustering. YOLOv11x achieved 0.78 mAP@0.5 at 101 FPS, enabling real-time processing suitable for field deployment. Field trials confirmed robust detection and consistent risk ranking in both uncluttered and cluttered urban environments, substantially reducing inspection time and minimizing missed defects compared to conventional manual methods. The framework provides scalable, data-driven support for city-wide monitoring and transparent, risk-prioritized maintenance of aging infrastructure.