The increasing complexity of urban systems has intensified the exposure of residential high-rise buildings to interconnected and cascading risks commonly conceptualized as a “polycrisis,” where multiple systemic disruptions interact simultaneously across environmental, infrastructural, technological, and socio-economic dimensions. This study develops an AI-enabled conceptual framework for polycrisis risk mitigation in residential high-rise buildings through a systematic and critical synthesis of existing literature on smart buildings, crisis management systems, and artificial intelligence applications in the built environment. The research integrates advancements in Internet of Things (IoT)-enabled infrastructures, predictive analytics, and multi-level crisis governance models to propose an adaptive resilience architecture capable of responding to dynamic urban risk environments.

Findings indicate that current building resilience approaches remain fragmented, with limited integration between real-time data acquisition systems, AI-based decision-making models, and evacuation optimization mechanisms. While smart building technologies demonstrate significant potential for operational efficiency and predictive maintenance, their application to multi-hazard crisis convergence remains underdeveloped. This study identifies critical gaps in interoperability, scalability, and ethical governance within AI-driven urban resilience systems. The proposed framework synthesizes these dimensions into a unified architecture combining predictive risk modeling, adaptive control systems, and multi-agent decision support.

The study contributes to urban resilience theory by bridging AI-enabled computational intelligence with crisis management strategies for high-rise infrastructure. It also highlights the necessity of integrated policy-technology ecosystems to address systemic vulnerabilities amplified under polycrisis conditions, as emphasized in global risk discourse (Torkington, 2023).