<p>Global climate change posed profound challenges to the stability and sustainability of urban energy systems. As densely populated and functionally complex hubs, cities are hotspots where rising energy demand intersect with increasing risks from extreme climate events. Therefore, it is necessary to review the evaluation framework of urban energy, methods, and strategies for improving urban energy resilience. This work reviewed the development characteristics and modeling approaches of urban energy systems, analyzed the impacts of extreme climate events on energy demand and infrastructure, and synthesized recent progress on resilience concepts, indicator frameworks and evaluation methods, highlighting their strengths and limitations. The results indicate that urban energy systems are increasingly vulnerable to extreme climate events, which significantly disrupt energy demand and infrastructure stability. Existing resilience assessment frameworks remain largely static and lack dynamic feedback mechanisms to capture system evolution. Artificial intelligence and big-data-driven approaches demonstrate strong potential to enhance real-time risk prediction, adaptive regulation, and intelligent recovery. Then, we proposed optimization pathways include spatial optimization of energy demand, diversified regulation of system structures and operations, and dynamic assessment and intelligent scheduling enabled by artificial intelligence and big data. We suggested that constructing an urban energy resilience system with capabilities of dynamic sensing, rapid recovery, and continuous adaptation is critical for enhancing urban energy security.</p>