This research endeavors to create an improved Automatic Number Plate Recognition (ANPR) system to meet the urgent need for dependable vehicle tracking. Using YOLOv8, YOLOv5, Easy Optical Character Recognition (EasyOCR), Deep Simple Online and Realtime Tracking (Deep SORT), and a specially designed License Plate Detector, our research investigates how these important technologies might be combined to improve the effectiveness of vehicle plate number identification. Novel methods for data augmentation, pre-processing, and optical character recognition (OCR) greatly improve detection accuracy and recognition performance, guaranteeing the system's resilience in difficult situations. A range of deep learning models, such as Convolutional Recurrent Neural Networks (CRNN), Single Shot Detection (SSD), Faster Region-Based Convolutional Neural Networks (Faster R-CNN), and several You Only Look Once (YOLO) versions, are used in the study to demonstrate how well they work for tracking and ANPR applications. Our work will proceed in new areas in the future, addressing real-time tracking issues by emphasizing hardware optimization, dynamic traffic algorithms, and improved multilingual OCR capabilities. The smooth integration of ANPR into traffic control infrastructure, continuous research into edge computing, and the deployment of stricter data security procedures demonstrate this dedication to the advancement of tracking technology. Our study helps to design an advanced ANPR system that not only satisfies the need for dependable vehicle tracking but also establishes the foundation for future developments in the area by giving priority to these important elements and technologies.