This paper presents a novel model for predicting car prices using various machine learning algorithms, including Random Forest, Linear Regression, KNN, ADABOOST, SVM, XGBoost, and Decision Tree. The study utilizes five datasets, four of which are sourced from Kaggle and Research Gate, and one newly generated from the Opensooq website, containing 8740 instances and 67 features. The evaluation metrics used are Mean Squared Error (MSE) and R-squared (R2) Score. The results indicate that the Random Forest algorithm achieves the highest R-squared value and the lowest MSE, making it the most effective model for car price prediction. The study highlights the importance of leveraging machine learning techniques to enhance the accuracy and reliability of car price predictions, which is crucial for various stakeholders in the automotive industry. Future work includes building a new model using an artificial neural network and expanding the dataset to improve its reliability.This paper presents a novel model for predicting car prices using various machine learning algorithms, including Random Forest, Linear Regression, KNN, ADABOOST, SVM, XGBoost, and Decision Tree. The study utilizes five datasets, four of which are sourced from Kaggle and Research Gate, and one newly generated from the Opensooq website, containing 8740 instances and 67 features. The evaluation metrics used are Mean Squared Error (MSE) and R-squared (R2) Score. The results indicate that the Random Forest algorithm achieves the highest R-squared value and the lowest MSE, making it the most effective model for car price prediction. The study highlights the importance of leveraging machine learning techniques to enhance the accuracy and reliability of car price predictions, which is crucial for various stakeholders in the automotive industry. Future work includes building a new model using an artificial neural network and expanding the dataset to improve its reliability.