The rise of Internet of Things (IoT) networks in smart cities has provided a range of advantages, such as real-time traffic management and optimized energy usage. Nonetheless, these networks encounter hurdles related to data traffic overload, fluctuating network conditions, and the necessity for scalable communication frameworks. Artificial Intelligence (AI)-driven routing protocols present a promising alternative by effectively overseeing data flow, minimizing latency, and maximizing resource utilization. This research paper examines the effectiveness of AI-based algorithms in improving routing performance within smart city IoT networks. It investigates several AI methods, including Machine Learning (ML) and Reinforcement Learning (RL), that react dynamically to network conditions, anticipate traffic loads, and determine optimal paths for effective data transmission. By incorporating these AI-enhanced protocols, smart cities can achieve lower energy usage, enhanced scalability, and greater dependability. Comparative simulations between traditional and AI-driven routing protocols indicate that AI-based approaches can considerably alleviate congestion and improve response times, even in densely populated urban IoT environments. This research provides valuable insights into the implementation of AI-enabled routing in smart city IoT networks, laying the foundation for more intelligent and sustainable urban infrastructures.