An examination of the specifications of technicalities and the customers perception reveals themselves as factors that boast the dynamic price settings of sports cars. This study provides a comprehensive analysis using the algorithms of machine learning to identify the correlations between various automobile elements and how much they are priced. In this particular case, decision trees, random forest, and linear regression have been employed to predict the costs of the sports vehicles relying on factors such as acceleration, horsepower, the size of a simple engine, acceleration measures, and model year. Consequently, the analysis of the prices indicated that non-technical factors such as brand awareness and layout of antique models are just as pivotal as evidentiary features like acceleration and horsepower, in terms of their influence on costs. Here we present the results which evidence the fact that decision forest and random forest are fairly accurate for trees based model and give better prediction outcomes than linear model in terms of capturing highly nonlinear patterns. These models clearly pointed to central role of features related to performance metrics as well as outlined the influence of the intangible characteristics, such as brand and reputation, history of the firm. This research lays the ground for subsequent studies to incorporate other factors such as customer behaviors and consumer economics in examining the pricing models in the sports car market. By using technical computational power such as state-of-the-art machine learning approach.