The pricing dynamics of sports cars are influenced by a complex interplay of technical specifications and market perceptions. This paper presents a comprehensive analysis using machine learning models to uncover the relationships between various car features and their impact on pricing. We employed linear regression, decision trees, and random forests to predict sports car prices based on features such as torque, horsepower, engine size, acceleration metrics, and model year. The study revealed that while technical features like torque and horsepower significantly affect prices, non-technical factors such as brand prestige and the allure of vintage models also play crucial roles. Our results demonstrate that tree-based models, specifically decision trees and random forests, provide high predictive accuracy, capturing complex non-linear relationships better than linear models. These models effectively highlighted the predominant influence of performance-related features while also suggesting the significant impact of intangible factors like brand and historical value. This study opens the door for future research to integrate broader variables, including consumer behavior and economic conditions, to refine the understanding of pricing strategies in the sports car market. By leveraging advanced machine learning technique.( Abstract )