The increasing frequency and severity of natural disasters—such as forest fires, earthquakes, snowstorms, tornadoes, and hurricanes—are significantly impacting U.S. cities and coastal communities, displacing thousands of people and incurring billions of dollars in government spending. To address these growing challenges, this project proposes the development of TRACE (Testbed for Disaster Resilience Auditing and Crisis Evaluation), an innovative cross-cutting platform integrating Social Cyber-Physical Systems (CPS), Internet of Things (IoT), Robotics, and AI/ML technologies. TRACE is designed to assist multidisciplinary search and rescue teams in accelerating mission-critical response and recovery operations in post-disaster scenarios. The project's integrative approach aims to build a scalable, multidimensional, and resilient AI-ready testbed to assess the effects of natural hazards—including fires, earthquakes, floods, hurricanes, and tornadoes—on the built environment (buildings, bridges), infrastructure (roads, utilities), and communities. By combining TRACE with scalable AI/ML algorithms, the project will support community-level disaster resilience through: (i) Characterization of risks and vulnerabilities, (ii) Anticipation of failures and losses, (iii) Data-driven planning and decision-making in partnership with civic agencies. Resilience metrics will be quantified at both system and application levels and reevaluated in terms of community strategies across the five d