Predictive maintenance, an anticipatory method for managing assets, has seen remarkable growth in recent times. The combination of Artificial Intelligence (AI) and Internet of Things (IoT) technologies offers a significant shift in this area. This research paper investigates the collaborative potential of AI and IoT in facilitating predictive maintenance within smart city frameworks. By utilizing the extensive data produced by IoT sensors alongside the analytical power of AI algorithms, it's feasible to foresee equipment breakdowns, improve maintenance timelines, and boost overall system dependability. This paper examines the essential elements of predictive maintenance systems that are based on AI and IoT, such as data collection, feature development, model training, and prediction creation. Furthermore, it addresses the obstacles and possibilities related to the implementation of these systems in urban settings. Through a detailed review of existing literature and practical examples, this paper seeks to offer meaningful insights into the latest advancements and future pathways in AI-IoT-based predictive maintenance for smart cities.