This paper presents a study on predicting car prices using various machine learning algorithms. The research utilizes five datasets, four of which are sourced from Kaggle and ResearchGate, and one is constructed from scratch using data from the Opensooq website, containing 8740 instances and 67 features. The algorithms tested include Random Forest, Linear Regression, KNN, AdaBoost, SVM, XGBoost, and Decision Tree. Evaluation metrics used are Mean Squared Error (MSE) and R-squared (R2) Score. The results show that the Random Forest algorithm achieved the highest R-squared score and the lowest MSE, indicating superior performance in predicting car prices. The study highlights the importance of data preprocessing, feature selection, and the use of appropriate algorithms for accurate predictions. The research also discusses the challenges involved in constructing datasets and selecting relevant features. The methodology includes data preprocessing, feature selection, and applying various machine learning algorithms to predict car prices. The evaluation of the algorithms based on MSE and R2 scores demonstrates that Random Forest outperforms other algorithms in terms of accuracy and reliability. The paper concludes that Random Forest is the most effective algorithm for car price prediction, with the highest R2 score and lowest MSE. Future work includes developing a new model using artificial neural networks and expanding the dataset to improve reliability. The study contributes to the field of machine learning by demonstrating the effectiveness of Random Forest in predicting car prices and provides insights into the application of machine learning techniques in the automotive industry.This paper presents a study on predicting car prices using various machine learning algorithms. The research utilizes five datasets, four of which are sourced from Kaggle and ResearchGate, and one is constructed from scratch using data from the Opensooq website, containing 8740 instances and 67 features. The algorithms tested include Random Forest, Linear Regression, KNN, AdaBoost, SVM, XGBoost, and Decision Tree. Evaluation metrics used are Mean Squared Error (MSE) and R-squared (R2) Score. The results show that the Random Forest algorithm achieved the highest R-squared score and the lowest MSE, indicating superior performance in predicting car prices. The study highlights the importance of data preprocessing, feature selection, and the use of appropriate algorithms for accurate predictions. The research also discusses the challenges involved in constructing datasets and selecting relevant features. The methodology includes data preprocessing, feature selection, and applying various machine learning algorithms to predict car prices. The evaluation of the algorithms based on MSE and R2 scores demonstrates that Random Forest outperforms other algorithms in terms of accuracy and reliability. The paper concludes that Random Forest is the most effective algorithm for car price prediction, with the highest R2 score and lowest MSE. Future work includes developing a new model using artificial neural networks and expanding the dataset to improve reliability. The study contributes to the field of machine learning by demonstrating the effectiveness of Random Forest in predicting car prices and provides insights into the application of machine learning techniques in the automotive industry.