As urban areas expand, the need for secure, efficient, and eco-friendly urban solutions becomes more pressing. Smart cities tackle these issues by utilizing the Internet of Things (IoT) to link physical infrastructure with advanced digital systems. This synergy improves the management of urban resources and greatly enhances the quality of life for residents. A key element in this advancement is the function of Artificial Intelligence (AI) in refining routing methods within IoT networks, allowing cities to adapt effectively to changing conditions. This paper examines the crucial elements involved in designing smart cities, stressing the significance of AI-driven routing techniques in IoT networks. We introduce a novel AI-IoT framework that prioritizes real-time monitoring and traffic management. By implementing IoT sensors throughout urban environments—on highways, bridges, and energy systems—the framework collects and analyzes real-time data to enhance vehicle routing. This optimization alleviates congestion, reduces fuel usage, and lessens emissions, supporting broader urban sustainability efforts. Our thorough simulations and case studies across various urban contexts reveal notable enhancements in traffic flow and environmental effects. Furthermore, we explore the role of Unmanned Aerial Vehicles (UAVs) within smart city infrastructure. We create a multi-objective routing strategy that produces efficient, non-dominated solutions by optimizing drone trajectories in IoT networks while addressing obstacles and restricted zones. These UAV routes are evaluated using advanced visualization tools, which assist in reconciling competing goals and ensuring smooth integration into urban environments. This study offers valuable perspectives for policymakers, urban planners, and transportation officials. It underscores the scalability and cost-effectiveness of AI-enhanced routing solutions that can improve connectivity and sustainability in smart cities.