Abstract
<jats:p>Traditional seismic rehabilitation, reliant on static designs and isolated hazard treatment, proves inadequate against today's overlapping natural hazards and sustainability imperatives. This chapter introduces a paradigm integrating Multi-Hazard Early Warning Systems (MHEWS) with AI-enhanced Structural Health Monitoring (SHM) for dynamic, lifecycle-oriented resilience. By fusing MHEWS data streams with real-time sensor data from retrofitted structures, artificial intelligence serves as the critical nexus. Advanced machine learning models analyze this multi-modal data for real-time vulnerability assessment and predictive damage simulation under compound threats, enabling context-aware mitigation. This anticipatory framework transcends conventional code-based approaches, embedding proactive intelligence into infrastructure systems. Ultimately, we reposition seismic rehabilitation as an adaptive component within a broader hazard-response ecosystem—enhancing public safety, optimizing resources, and advancing sustainable development in an era of compounded environmental challenges.</jats:p>