With the exponential growth in urbanization and industrialization, the volume of vehicles traversing road networks has surged, presenting multifaceted challenges for urban management and public safety. The escalating number of vehicles not only exacerbates traffic congestion and demands for traffic and parking management but also amplifies the incidence of traffic violations, accidents, and criminal activities. To address these issues there’s a growing interest in Automatic Number Plate Recognition (ANPR) systems. This research presents a comprehensive system leveraging convolutional neural networks for license plate detection, vehicle tracking, and optical character recognition. We employ state-of-the-art techniques including YOLOv9 for object detection, DeepSORT for tracking, and PaddleOCR for character reading. Our model achieves remarkable performance with a 98.6% mAP50 score for license plate detection, maintaining a precision of 98.3% and recall of 95.8%. Additionally, the proposed system demonstrates exceptional performance on test videos, accurately detecting 97.4% of characters. These findings highlight the promising potential for future ANPR technology utilizing YOLO.