This paper provides a comprehensive review of the applications and benefits of predictive analytics in supply chain management (SCM). It explores how predictive analytics enhances efficiency, decision-making, and overall performance through demand forecasting, inventory optimization, and supply chain visibility. The authors discuss the historical evolution of predictive analytics in SCM, highlighting key concepts and definitions such as predictive modeling, data mining, and machine learning. They outline the methodology used to conduct the literature review, including the selection criteria and search strategies employed. The paper then delves into the practical implications of implementing predictive analytics, supported by real-world examples and case studies. It emphasizes the transformative impact of predictive analytics on SCM processes, including improved forecasting accuracy, optimized inventory levels, and enhanced supply chain visibility. Despite its benefits, the paper also addresses challenges and limitations, such as data quality, integration complexity, and the "black-box" nature of advanced algorithms. Finally, the authors discuss future trends and developments, including the integration of AI and ML, blockchain technology, and real-time analytics, and the potential for Predictive Analytics as a Service (PaaS). Ethical considerations, particularly in data privacy, fairness, accountability, and environmental impact, are also discussed. The paper concludes by emphasizing the importance of ethical practices and continuous learning in the dynamic field of predictive analytics in SCM.This paper provides a comprehensive review of the applications and benefits of predictive analytics in supply chain management (SCM). It explores how predictive analytics enhances efficiency, decision-making, and overall performance through demand forecasting, inventory optimization, and supply chain visibility. The authors discuss the historical evolution of predictive analytics in SCM, highlighting key concepts and definitions such as predictive modeling, data mining, and machine learning. They outline the methodology used to conduct the literature review, including the selection criteria and search strategies employed. The paper then delves into the practical implications of implementing predictive analytics, supported by real-world examples and case studies. It emphasizes the transformative impact of predictive analytics on SCM processes, including improved forecasting accuracy, optimized inventory levels, and enhanced supply chain visibility. Despite its benefits, the paper also addresses challenges and limitations, such as data quality, integration complexity, and the "black-box" nature of advanced algorithms. Finally, the authors discuss future trends and developments, including the integration of AI and ML, blockchain technology, and real-time analytics, and the potential for Predictive Analytics as a Service (PaaS). Ethical considerations, particularly in data privacy, fairness, accountability, and environmental impact, are also discussed. The paper concludes by emphasizing the importance of ethical practices and continuous learning in the dynamic field of predictive analytics in SCM.