The blistering development of the concept of smart cities has escalated the use of Internet of Things (IoT) gadgets to monitor the urban facilities in real time, and this produces massive amounts of data that compromise the conventional cloud-based structures in terms of high latency, high bandwidth occupancy, and high energy consumption. This paper presents and analytically judges an edge-enhanced IoT architecture that will be used to improve energy efficiency and real-time performance of smart city monitoring systems. The study adopts a quantitative, model-based study that is based on all secondary data and analyzes a three-layer IoT-edge-cloud system and evaluates system performance based on end-to-end latency, energy usage, bandwidth usage, computational load allocation, and data reduction efficiency. The comparative assessment and analytical modelling show that edge-level data processing is a significant way to cut down processing and transmission time, decrease overall energy usage despite extra use of edge-layers, reduce bandwidth requirements by data reduction, and push out the computation load off the centralized cloud servers. The results show that the edge assisted IoT architectures are more scalable, responsive and energy-efficient than cloud-centric systems and therefore provide a robust and sustainable solution to large-scale real-time urban city monitoring and future urban digital transformation.