The implementation of machine learning (ML) approaches in supply chain management (SCM) has gained increasing attention in recent years due to the potential benefits it offers in enhancing supply chain performance. However, the success of implementing ML in SCM depends on various factors, including data quality, availability, and integration, as well as the selection of appropriate ML algorithms and the integration of results into decision-making processes. This critical study aims to analyze the factors influencing the implementation of ML approaches for better performance in the SCM process. To achieve this objective, a systematic literature review was conducted to identify and analyze previous studies related to ML implementation in SCM. The review highlights the importance of data quality, availability, and integration, as well as the selection of appropriate ML algorithms and the integration of results into decision-making processes. Moreover, the review identifies several challenges that may hinder the successful implementation of ML in SCM, including data privacy and security, lack of expertise and skills, and the need for a cultural shift towards data-driven decision-making. The study also proposes a conceptual framework that incorporates the identified factors and challenges to guide organizations in implementing ML approaches in SCM. The framework emphasizes the importance of establishing a data governance structure to ensure data quality and security, investing in data analytics capabilities and talent, and creating a culture of data-driven decision-making. The framework also suggests that organizations should consider the ethical implications of using ML in SCM and ensure transparency and fairness in decision-making. The results of this study provide valuable insights into the factors that influence the successful implementation of ML approaches in SCM and offer practical guidelines for organizations to overcome the challenges and achieve better performance. The study contributes to the growing literature on the application of ML in SCM and provides a foundation for future research in this area.