This research presents a comprehensive approach to enhancing road safety and Traffic Law enforcement through advanced multi-modal image analysis. The methodology encompasses three key components: Vehicle Traffic Signal Violation Detection, Pedestrian Counting, and Driver Identification, with an extension for Vehicle License Plate Recognition. When a vehicle violates traffic signal laws, the system intelligently assesses the situation. Initially, it checks for the visibility of the driver’s face, and if obscured, it proceeds to detect and recognize the vehicle’s license plate. Furthermore, pedestrian counting near zebra crossings is employed to ensure the safety of road users. The methodology leverages state-of-the-art object detection and recognition models, including Easy OCR, and YOLOv3, for robust and accurate results. The integration of these components creates a powerful system for traffic signal violation detection, pedestrian safety assessment, and driver identification.