A car's price estimate has drawn a lot of attention because it calls for a lot of work and the expert's understanding in the topic. For the dependable and accurate prediction, a large number of unique attributes are considered. We employ the Ridge, Lasso, and Random Forest Regressor in Linear Regression, Gradient Boosting Regressor to develop a model for predicting used automobile prices. However, the aforementioned methods were used in concert. Before beginning the process of constructing the model, in order to assist people, comprehend the dataset, this project displayed the data. This data collection was partitioned and altered to suit the regression in order to guarantee the model's performance. Each regression's performance was assessed using R-square. The final model has a higher forecast accuracy and more components related to used cars than the earlier research. Despite the decline in the market for new cars, the used car market has kept growing. Because used automobiles are more affordable and may be sold again after a few years of use for a profit, the majority of people choose to purchase them. The price of used automobiles is influenced by factors including fuel type, colour, model, mileage, engine, gearbox, number of seats, etc. The cost of used cars on the market will continue to fluctuate. As a result, to predict the cost of used cars, an evaluation model is required.