In this paper, the main objective is to create an algorithm that uses machine learning to accurately estimate automobile prices using information about the data model, base anticipated cost, actual price, market price, and demand. When customers submit the basic specifications of a used automobile, the model can make accurate calculations after testing and training it on a variety of cars and models. Real-time data analysis greatly benefits from machine learning and data science, especially with Python, which enables rigorous testing, training, and accurate result generation. This project attempts to highlight how machine learning can be applied to a simple algorithm and how computers can self-train to create results without tedious programming and the model will be trained by choosing crucial attributes to train the machine literacy model. The model will be trained with a range of machine learning methods, including Random Forest and Linear Regression Among the performance metrics that will be used to judge the model’s effectiveness are perfection, recall, and best position. By altering the model’s characteristics and parameters, performance will be improved as well.