Automatic Number Plate Recognition (ANPR) technology has offered a good solution tool for automating toll collection system processing and contributing solutions to complications such as traffic congestion, environmental problems, and operational inefficiencies. The investigation focuses on improving and organizing an ANPR-based system to allow smooth toll charging and letting cars pass through toll gates without pausing. The suggested system uses a deep learning algorithm and developed image processing techniques to consistently detect and recognize license plates of vehicles in real-time, even in difficult conditions, for instance, fast motion, low lighting, and bad weather. The ANPR system is connected to a centralized database and an automated billing system, confirming secure and real-time transactions. This method removes the need for manual intervention, decreases wait times, and improves user convenience. In addition, the system is designed adaptable to manage large traffic volumes while preserving both accuracy and efficiency. Extensive experimental evaluations were conducted to validate the system’s performance, demonstrating high recognition accuracy, robust reliability, and minimal processing delays. By automating tolling operations, this solution improves the overall user experience and contributes to reduced vehicle emissions and energy consumption, aligning with sustainable transportation goals. The findings of this research underscore the potential of ANPR technology to revolutionize tolling systems and provide a blueprint for its large-scale deployment in smart transportation networks worldwide.