aspg-1-698-1791419238 20261008002718 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group American Journal of Business and Operations Research AJBOR 2770-0216 2692-2967 2021 2 1 Amal F.Abd El-Gawad Faculty of Engineering, Zagazig University, EGYPT Shereen Zaki Department of Decision Support, University of Zagazig, EGYPT Esraa Kamal Faculty of Computers and Informatics, Zagazig University, EGYPT Machine learning arose from the increasing ability of machines to handle large amounts of data over the last two decades, and some machines could also identify hidden patterns and complicated associations that humans couldn't, allowing them to make rational and precise decisions, especially for disruptive and discontinuous data. In several areas of decision-making, machines could produce more reliable outcomes than humans and have already begun to replace them. Machine learning, which is widely recognized as a breakthrough technology, has recently made significant progress in improving supply chain management processes and efficiency. From planning to delivery, machine learning may be applied at different stages of the supply chain management process. Machine learning types are supervised, unsupervised, reinforcement. Each type has many tools which are discussed below in detail. This paper presents a detailed survey on machine learning techniques for supply chain management including supply chain and supply chain management interpretation, machine learning definition, its types, and some algorithms that belong to it. 2021 24 38 698 https://creativecommons.org/licenses/by/4.0/ 10.54216/AJBOR.020103 https://www.americaspg.com/journal/1/article/698 https://www.americaspg.com/storage/7213.pdf