The official collection of numbers and letters shown on the front and rear of a road vehicle is called a license plate. License plate recognition is crucial for many applications, including traffic monitoring, vehicle tracking, and law enforcement. Automatic License Plate Recognition (ALPR) is one of the applications that benefited tremendously from convolutional neural network (CNN) processing, which is now the de facto processing method for complex data. This paper offers a comprehensive analysis and comparison of deep learning-based approaches for identifying license plates. The study also looks a dataset from Kaggle, pre-process the dataset for removing noise and artifact, utilized to train these models into test, train, and validation dataset. Additionally, the study examines the difficulties and prospective possibilities in deep learning-based license plate recognition, highlighting the potential uses of cutting-edge innovations such object detection using attention mechanisms and graph convolutional networks. In this study we utilized six transfer learning model which is VGG16, VGG19, DenseNet121, AlexNet, EfficientNet, and YOLOv4 where DenseNet121 provide the best or highest accuracy 98.66%. The goal of the study is to guide researchers and industry professionals toward powerful license plate detecting systems by employing deep learning approaches by way of this evaluation and analysis.