As urban areas begin to implement Internet of Things (IoT) networks for transportation, new obstacles emerge in enhancing routing. Effective routing is crucial for ensuring efficient data communication among devices, sensors, and vehicles while minimizing energy use and delays. However, fluctuating conditions—such as traffic and network demands—complicate the process of making routing choices. Traditional methods frequently struggle to adapt in these scenarios. This research examines various strategies for improving routing, including techniques like Ant Colony Optimization (ACO) and Dijkstra's Algorithm. We suggest a dynamic routing framework that reacts to real-time circumstances, with an emphasis on conserving energy and guaranteeing dependable data transmission. We evaluate its performance based on critical metrics like packet delivery rates, response times, and energy use in simulated smart city environments. Our findings indicate that this system effectively lowers data delivery delays and enhances energy efficiency in comparison to conventional routing approaches. Adjusting routing paths in response to current conditions makes the system adaptable and trustworthy—traits that are essential for smart city initiatives. This study offers valuable insights for urban planners and IoT developers aiming to enhance transportation efficiency. Our results underscore the significance of adaptable algorithms in addressing the evolving demands of smart cities, fostering more sustainable and productive urban settings.