The most important aspect aimed to develop a prediction model that could calculate a used car’s fair value based on a variety of factors, including the make, model year, fuel type, price, and miles. This study incorporated a lot of data science and machine learning approaches in the process of developing the model, which has a lot of promise and is available to manufacturers and other players in the used automobile market. The information used in this analysis came from a database of used car advertisements. To provide the best prediction accuracy, several regression techniques were evaluated, including decision tree regression, random forest regression, support vector regression, polynomial regression, and linear regression. To gain a deeper understanding of the data set, the research included substantial preprocessing and data visualization prior to the modeling step. To make it possible to evaluate the performance of each regression model, the data set was divided and modified to satisfy their specifications. The regression models were evaluated using the R-squared statistic. The final model presented in this study contains a wider range of used automobile attributes and provides better prediction accuracy when compared to previous investigations. This research promotes the used car industry and aids in the making of more informed judgments by providing a better model for pricing used cars. The benefits of sound data analysis and a range of regression techniques are used to increase the accuracy of such predictive models.