Fog computing has emerged as a solution to bandwidth and latency constraints in dynamic Internet of Things (IoT) environments; however, large-scale deployments are still limited. Today, abundant urban IoT data, mature 5G networks, and the real-time demands of digital twin cities (DTCs) have created a landscape well-suited to fog computing. This study examines the transition from centralized cloud computing to fog computing as a foundational enabler of real-time DTC systems in urban environments. By redistributing computational workloads across fog nodes, a framework is proposed that significantly reduces data transmission latency by up to 67% and enhances network efficiency by approximately 65–67% compared with conventional cloud-computing architectures. Central to this study is the integration of digital twin technology with a three-layer edge-fog-cloud hierarchy, which enables continuous synchronization between virtual and physical urban environments and supports autonomous real-time decision-making through DTC technologies. The proposed model was validated using iFogSim to systematically compare fog- and cloud-based scenarios in a modeled 25 km²residential city, with surveillance camera counts scaled from 16 to 80 across four operational zones. The results demonstrate the potential of fog computing to enhance the quality and efficiency of smart urban monitoring and management, thereby providing a scalable and sustainable foundation for highly efficient infrastructure in next-generation cities.