As urban areas rapidly transform into smart cities, integrating Internet of Things (IoT) devices has become essential for managing infrastructure, resources, and public services. However, synchronizing large IoT devices to function cohesively in real-time presents significant challenges, primarily due to latency, bandwidth constraints, and data overload. Traditional cloud-based solutions, while powerful, fall short in supporting the low-latency requirements needed for efficient IoT synchronization within smart cities. Edge computing has emerged as a viable alternative by decentralizing data processing, enabling computations closer to the data source, reducing latency, and improving system resilience. This paper investigates the use of edge computing for IoT synchronization in smart cities, focusing on how it supports real-time data exchange and enhances system reliability. We examine edge computing architectures and synchronization models tailored for IoT environments, identifying configurations that optimize latency, data consistency, and energy efficiency. Additionally, we explore the implications of edge computing on data privacy and bandwidth savings, which are critical considerations in urban deployments where devices generate high-frequency data. Our study employs a simulated smart city environment to measure the performance of edge computing in synchronizing IoT devices, comparing it with traditional cloud models. Results indicate that edge-based systems achieve a 40% reduction in latency and a 25% improvement in data consistency, thus providing a scalable solution for smart cities. These findings underscore the potential of edge computing to address critical IoT synchronization challenges, offering a robust framework that enables faster response times and more efficient resource management. This study’s insights contribute to the growing field of smart city technologies, showcasing edge computing as a foundational approach to support synchronized, real-time IoT operations essential for sustainable urban growth.