Automatic Number Plate Recognition (ANPR) systems have become essential for various applications, including traffic monitoring, law enforcement, and toll collection. This paper presents a comprehensive study of an ANPR system that utilizes advanced image processing techniques and machine learning algorithms to achieve high accuracy in license plate detection and recognition. The proposed system employs a multi-step approach: image acquisition, preprocessing, plate localization, character segmentation, and optical character recognition (OCR). By integrating deep learning models for feature extraction and classification, the system demonstrates improved performance in diverse environmental conditions. Experimental results show that the proposed ANPR system achieves a recognition accuracy of over 95%, indicating its potential for real-world applications. Furthermore, the paper discusses challenges faced in ANPR implementation, including variations in plate design, illumination conditions, and occlusions, and suggests future directions for research to enhance robustness and efficiency.