Sales prediction is the current numero trend in which all the business companies thrive and it also aids the organization or concern in determining the future goals for it and its plan and procedure to achieve it. The data about car sales are derived from various sources sales of cars does not contain any independent variable since various factors such as horse power; model, width, fuel type, height, price, city-mileage, highway-mileage and manufacturer are the various features that influence the sales. In car sales prediction we first implement the methodology of analytic hierarchy process in order to get varied idea about how well the various criteria’s in our dataset works and after this we apply the machine learning algorithms such as Linear regression, Random tree to get the best clusters and we process them in to random forest to get best accurate feature out of it. which is ultimately followed by Technique for Order of preference by similarity to ideal solution (TOPSIS) an tool which helps the researcher to arrive at a verdict when he/she faces the one or more pattern selection problem and the final resultant derived from all these methods gives the fittest feature which influences the customer in purchasing the car which indirectly gives the company or the research market a result in predicting the future sales for cars. Hence this paper not only provides its users with some stats, it also serves an guiding guardian by providing accurate results for purchasing a car. © BEIESP.