The objective of this paper is to integrate Artificial Intelligence (AI) and the Internet of Things (IoT) for intelligent traffic control in smart cities. We employed the YOLOv8n model to detect speed limit signs, traffic lights, speed bumps, and potholes. Data was collected from Morocco, India, and Europe to train the model. Our model demonstrated an accuracy of 95.5% in detecting these traffic elements. To deploy the system, we implemented it on a small vehicle equipped with a Raspberry Pi 4. The vehicle, equipped with a webcam, can successfully detect objects and issue audio alerts. Additionally, the system autonomously interacts with the vehicle’s speed: it slows down upon detecting a speed bump, stops at a red traffic light, and moves forward at a green light. This work showcases the potential for improving traffic safety and efficiency in smart cities through the integration of AI and IoT technologies.