Abstract:. Humans are the superior creature on the earth, they have invented the new technologies and inventions a one of the greatest inventions is vehicles (2vechicle, 4 vehicle , heavy loads vehicles , commercial vehicles) which saves time , money and muscle work. The most important things we want is less pollution and less accident on road. Humans have made ease of life but as the population is increasing day by day the more dependent on vehicles the pollution caused by the vehicles effects the environment as well as living creature life , the pollution caused by the vehicles such as carbon monoxide(CO sulfur dioxide(SO2) and hydrocarbons affects nature and living life the government have many precautions and polices to control such as PUC certificate, even odd vehicles, electric vehicles subsite. But mainly buyer mainly focused on speed,torque,safety and milage while buying but they also forget to check Greenscore for vehicles which is also essential while buying .which tells about which vehicles is greenest and meanest ranking. This research is all about the identification and selection of vehicle(cars) using machine learning As a result, we employed a CNN network with multiple layers, including different type layers, ReLU, pooling layers, dense layers, and so on. We also use batch normalization and dropout layers to prevent the model from becoming overfit. To improve the accuracy of the outcome, we applied augmentation techniques. The effect of employing Max polling in CNN for feature mapping and reducing overfitting is shown below. With a 5 CNN hidden layer model, while working with different dataset we have achieved 93 percent training accuracy and 86 percent testing accuracy with one dataset while another we have achieved 99 percent training accuracy and 95 percent testing accuracy but having the same model and no. of epochs . The model's output will aid in prediction and selection of greenscore cars .