Our model provides an insight into the carbon emission emitted by automobiles on an overall basis. It can be used by an individual to know the carbon emitted by their vehicle on a periodic basis so that they can be more cautious about their vehicle usage. Our model also calculates the no of trees required according to the carbon emission which will help the user to identify the no of trees to be planted on an average. Our project is to study and evaluate the dataset containing information about the carbon emission of various automobiles with attributes differentiating them on the basis of engine-size, fuel type, model and make of the vehicle and fuel consumption. The carbon emission of a particular automobile is calculated on the basis of no. of cylinders, engine size and fuel consumption. The algorithm used to predict whether the carbon emission falls into the category of high, average or low is K-means clustering which is used to classify them into the above mentioned categories. Moreover we have used multiple regression using dependent variables as co2 emissions and independent variables being no. of cylinders, engine size and fuel consumption to predict co2 emissions. The model also calculates the no of trees required to nullify the effect of co2 emitted by a particular automobile over a given period of time which is taken from the user. This model can be used on a larger level by the RTO [Road Transport Office ] to predict the engine which produces maximum carbon-dioxide emissions. This can be used to calculate carbon emissions overall and trees required so as to improve the global warming condition.