This research introduces an artificial intelligence-driven framework designed to automate vehicle detection, tracking, and speed estimation in restricted traffic zones. The system leverages YOLOv8 for high-precision object detection, ByteTrack for robust multi-object tracking, and OpenCV for video processing within a Flask-based web application. A perspective transformation module maps pixel coordinates to real-world metrics, enabling accurate velocity and distance calculations critical for zones such as schools and hospitals. Experimental results demonstrate a detection precision of 0.94, a tracking success rate of 0.91, and a speed estimation mean absolute error (MAE) of 2.5 km/h under varying traffic and environmental conditions. By integrating open-source technologies, the proposed system is scalable, cost-effective, and adaptable, contributing significantly to intelligent transportation systems and urban road safety.