A significant number of cars are purchased and sold annually as a result of the price advantage. Reliable used car price forecasting became essential to understand the vehicle's value and gained popularity. This involves many factors such as make, model, condition, and other features of the used car. These attributes are to be properly processed to get accurate predictions. This work's main goal is to estimate used automobile prices utilizing factors that have a significant impact on the price. Null, redundant, and missing values are found and processed using data mining techniques. These values should be eliminated, and significant qualities should be taken into account. Three regressors—the Random Forest Regressor, the Linear Regression, and the Bagging Regressor—are assessed, trained, and contrasted in this supervised learning study utilizing a benchmark dataset. The Random Forest Regressor scored the highest (95%), followed by the MAE (0.0008), RMSE (0.0378), and MSE (0.025) among all the experiments. Apart from Random Forest Regression, Bagging Regression also fared well, scoring 88%, and Linear Regression, scoring 85%. All studies employed an 80/20 train-test split with 40 random states.