Understanding the factors that drive car prices is crucial for both manufacturers and consumers in today’s competitive automotive market. In this research, a stepwise multiple linear regression model is developed to investigate the factors that significantly influence car prices. By analysing a dataset of 205 observations, the model identified seven key predictors: engine size, curb weight, peak RPM, stroke, compression ratio, car width, and horsepower. These variables collectively explain 84.5% of the variation in car prices. Statistical tests validate the model, although the presence of multicollinearity indicates a need for careful interpretation. The study’s outcomes provide valuable insights for automotive manufacturers and consumers by highlighting the importance of these attributes in vehicle valuation. This research contributes to the domain by clarifying the complex interdependencies of car features and their combined effect on pricing, informing both strategic industry pricing and consumer decision-making.