Urban centers across India increasingly face challenges from unauthorized heavy vehicle movement in protected zones—especially during restricted hours—leading to congestion, road damage, and increased accident risk. Existing enforcement mechanisms rely heavily on manual monitoring and static checkpoints, which are not scalable, often inaccurate, and prone to delays in penalty processing. This paper presents a real-time AI-powered system that directly addresses this enforcement bottleneck by automating the detection and penalization of heavy vehicle violations. Leveraging YOLOv11 for object detection and Automatic Number Plate Recognition (ANPR), the system identifies heavy vehicles from live surveillance footage and extracts vehicle registration details, and cross-verifies against predefined time-based access rules for protected zones. Upon detecting a violation, the system automatically generates a fine and updates a centralized dashboard for enforcement and administrative review. In addition to enforcement, the system includes a route optimization module that utilizes real-time traffic analytics and OpenStreetMap routing APIs to suggest alternate, low-congestion routes for heavy vehicles thereby reducing the probability of future violations while improving traffic flow efficiency.