The research project aims to develop and implement an automated number plate recognition (ANPR) system to enhance traffic control and transportation efficiency. As the number of vehicles on the road continues to grow, the need for an intelligent and automated traffic monitoring system becomes increasingly crucial. The proposed ANPR system leverages machine learning and computer vision algorithms to accurately detect and recognize vehicle license plates in real time. Key functionalities include license plate localization, character segmentation, and alphanumeric character recognition. The system is designed to handle challenges such as partial occlusions, varying angles, and visual distortions. A large, annotated dataset is used for training and evaluation, ensuring high accuracy and adaptability. Successful deployment of the system is expected to enhance law enforcement, strengthen security, and improve traffic flow. Additionally, its applications in smart city initiatives, parking management, and toll collection contribute to a more efficient urban transportation system. The ANPR technology captures images of vehicles passing through checkpoints or surveillance areas and extracts license plate data for rapid identification. With recent advancements significantly improving speed, accuracy, and integration with databases and surveillance networks, ANPR has become a vital tool for modern traffic management.