Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/1870 2018 2018 Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt Esraa Esraa Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt Amal F. Abdel Abdel-Gawad Computer and Systems Department, Electronics Research Institute ,Giza , Egypt Basem Ibraheem Decision Support Department, Faculty of Computers and Informatics Zagazig University, Zagazig, 44519, Egypt Shereen Zaki Demand forecasting is a crucial aspect of managing the supply chain, as it helps companies optimize inventory levels and minimize expenses related to inventory shortages. In recent years, machine learning (ML) algorithms have gained popularity for demand forecasting, as they can handle large and complex datasets and provide accurate predictions. Precise demand prediction for car brands is vital for companies to minimize costs and prevent inventory shortages. The demand for distributing cars is a critical component of inventory management. However, estimating demand for new car sales is difficult due to its continuous nature. To address this challenge, a study was conducted to train, test, and compare the performance of five machine learning algorithms (Random Forest, Multiple Linear Regression, k-Nearest Neighbors, Extreme Gradient Boosting, and Support Vector Machine) using a benchmark dataset. Among all the experiments, the Support Vector Machine algorithm achieved the highest accuracy score of 71.42%. Moreover, Multiple Linear Regression performed well, with an accuracy score of 66.66%. On the other hand, the Extreme Gradient Boosting algorithm had the lowest accuracy score of 42.85%. All experiments used a train-test split of 7525. 2023 2023 24 37 10.54216/FPA.120102 https://www.americaspg.com/articleinfo/3/show/1870