Internet of Things (IoT)-based smart city grids have the potential to revolutionize urban infrastructure management by enabling real-time monitoring and efficient resource allocation. However, challenges such as high latency, data overload, and the need for instantaneous decision-making hinder their effectiveness. This paper presents a framework for optimizing smart city grids using AI and edge computing. Artificial Intelligence (AI) enables advanced analytics and predictive maintenance, while edge computing allows data processing to occur closer to IoT devices, minimizing latency and reducing the load on central servers. The proposed approach integrates these technologies to optimize power distribution, traffic management, and environmental monitoring, leading to enhanced efficiency and reliability of city services. The results demonstrate improved resource management and quicker response times, showcasing the benefits of this approach for future smart city implementations. These findings underscore the importance of integrating AI and edge computing into IoT systems for more resilient and adaptive urban environments.