The convolutional neural network (CNN) is a form of artificial neural network that has become very popular in computer vision. We proposed a convolutional neural network for classifying common types of vehicles in our country in this paper. Vehicle classification is essential in many applications, including surveillance protection systems and traffic control systems. We raised these concerns and set a goal to find a way to eliminate traffic-related road accidents. The most challenging aspect of computer vision is achieving effective outcomes in order to execute a device due to variations of data shapes and colors. We used three learning methods to identify the vehicle: MobileNetV2, DenseNet, and VGG 19, and demonstrated the methods detection accuracy. Convolutional neural networks are capable of performing all three approaches with grace. The system performs impressively on a real-time standard dataset—the Nepal dataset, which contains 4800 photographs of vehicles. DenseNet has a training accuracy of 94.32% and a validation accuracy of 95.37%. Furthermore, the VGG 19 has a training accuracy of 91.94% and a validation accuracy of 92.68%. The MobileNetV2 architecture has the best accuracy, with a training accuracy of 97.01% and validation accuracy of 98.10%.