License Plate Recognition (LPR) is an essential part of contemporary Intelligent Transportation Systems (ITS), facilitating automatic vehicle identification for traffic control, law enforcement, and security purposes. Conventional LPR systems are based on image preprocessing, character segmentation, and Optical Character Recognition (OCR), but tend to be plagued by issues like plate orientation, occlusions, and changing environmental conditions. To overcome these limitations, deep learning-based methods such as Convolutional Neural Networks (CNNs), YOLO, and Transformers have been used to improve accuracy and efficiency. This paper suggests a sophisticated LPR system using Easy-OCR with the CRAFT model for strong character recognition. The process includes image preprocessing, license plate localization, and deep learning-based text extraction. Experimental tests exhibit a recognition rate of 97% with an average processing time of 1–2 s, surpassing the performance of traditional OCR-based systems. The research shows the potential for AI-based LPR solutions in real-time contexts in smart city infrastructure, autonomous tolling, and traffic monitoring. Future enhancements encompass incorporating cloud-based databases and AI-driven multitask learning to cover entire vehicle recognition.