Accurate prediction of automobile prices is important for good decision-making by automobile market participants and business competition. This study presents a comparison of three popular regression methods (linear regression, random forest regression, and decision tree regression) used for traffic cost estimation. The model is trained and analyzed using a comprehensive database containing various features such as car make, model, year, mileage, engine size and other irrelevant factors. Thanks to detailed data analysis, model design and preliminary procedures, the data becomes ready for modeling. Our regression models are then implemented and improved using cross-validation techniques to improve their performance. Statistical measures such as mean error (MAE), mean square error (MSE), and R-squared were used to evaluate the prediction accuracy of each model. The results show that random forest regression outperforms linear regression and decision tree regression in terms of prediction accuracy. Random forest regression shows excellent performance in handling non-linearities, interactions between features, and outliers present in the dataset. Its conditions allow the decision tree to reduce its bounds, resulting in good predictions. Linear regression, although simple and interpretable, often performs poorly when faced with relationships between features and target variables. Although decision tree regression is capable of capturing interactions, it can suffer from overfitting and poor generalization. This study provides useful information on the advantages and limitations of different methods for estimating traffic costs. It provides practical advice to automotive industry participants on selecting appropriate regression models for accurate and reliable vehicle price prediction. In summary, this study enables the estimation of vehicle costs by comparing regression methods. Using these insights, stakeholders can make informed decisions that will ultimately improve pricing strategies and market competitiveness in the automotive industry.