The aim of this study is to compose a machine learning model for the prediction of used car prices. One of the vital parts purchase in the used car market is the establishment of good price for the car. This demands a market forecast from all the players—buyers, sellers, dealers, auto finance companies. Making use of several machine learning methods, the factors affecting used car prices, namely, brand, model, year of manufacture, odometer reading, transmission, condition, and market demand, are analyzed. We conducted a series of tests on various machine learning methods, and found the best model that suits for the prediction of selling price. This research provides a comparative analysis of model performance and underlines the role of feature selection in the model building process when it comes to the improvement of predictive accuracy. The proposed model we have developed results in a competitive advantage for the companies through the utilization of data-driven pricing techniques, which help stakeholders make more scientifically-based decisions. This study opens the gate of predictive analytics in the automotive resale market field and it pioneers the data-driven auto pricing techniques.