Accurately estimating car prices is a matter of utmost importance in the motor vehicle industry, as it influences manufacturers’ choices, dealers’ prices, and buyers’ purchasing power. This research compares conventional regression techniques and contemporary deep learning models of car price estimation. It assesses their performance on major metrics such as MAE, MSE, and R squared (R2). Simple regression models are compared with DNNs to estimate the best approach. The research is on car attributes such as brand, model, year, mileage, fuel type, and transmission to perform intensive analysis. Linear regression and decision trees are easy to interpret but have poor predictability. Ensemble techniques such as random forests are more accurate but overfit. Deep learning models, particularly DNNs, are more predictable when learning complex nonlinear patterns, but at a high computational expense. The results indicate the trade-offs involved in terms of accuracy, interpretability, and computational expense in the identification of the best model for the estimation of the price of the car. This research contributes to the existing literature on machine learning applications in the motor vehicle industry, providing insight into the optimization of future price prediction methods.