Predictive analytics has become a transformative force in supply chain management (SCM), enhancing efficiency, decision-making, and overall performance. This review explores the applications and benefits of predictive analytics in SCM, focusing on demand forecasting, inventory optimization, and supply chain visibility. The integration of predictive analytics allows organizations to forecast demand accurately, optimize inventory levels, and improve supply chain visibility, leading to cost reduction, better decision-making, and enhanced risk management. However, challenges such as data quality, integration complexity, and model limitations must be addressed to fully realize the potential of predictive analytics. The historical evolution of predictive analytics in SCM has seen a shift from traditional statistical methods to advanced AI and machine learning algorithms. These technologies enable real-time data analysis, improving the accuracy and timeliness of predictions. The methodology of this review involved a systematic search of academic databases, focusing on recent and relevant studies. The findings highlight the practical implications of predictive analytics in various SCM domains, supported by case studies and real-world examples. Future trends in predictive analytics include the integration of AI and machine learning, blockchain technology for enhanced transparency, and the development of predictive analytics as a service (PAaaS). These advancements are expected to make predictive analytics more accessible and efficient. Ethical considerations, such as data privacy, fairness, and environmental impact, are crucial in ensuring responsible use of predictive analytics in SCM. In conclusion, predictive analytics is a strategic tool for modern SCM, offering significant benefits but requiring careful implementation to address challenges and ethical concerns. Organizations that adopt predictive analytics effectively can achieve greater efficiency, resilience, and competitiveness in their supply chain operations.Predictive analytics has become a transformative force in supply chain management (SCM), enhancing efficiency, decision-making, and overall performance. This review explores the applications and benefits of predictive analytics in SCM, focusing on demand forecasting, inventory optimization, and supply chain visibility. The integration of predictive analytics allows organizations to forecast demand accurately, optimize inventory levels, and improve supply chain visibility, leading to cost reduction, better decision-making, and enhanced risk management. However, challenges such as data quality, integration complexity, and model limitations must be addressed to fully realize the potential of predictive analytics. The historical evolution of predictive analytics in SCM has seen a shift from traditional statistical methods to advanced AI and machine learning algorithms. These technologies enable real-time data analysis, improving the accuracy and timeliness of predictions. The methodology of this review involved a systematic search of academic databases, focusing on recent and relevant studies. The findings highlight the practical implications of predictive analytics in various SCM domains, supported by case studies and real-world examples. Future trends in predictive analytics include the integration of AI and machine learning, blockchain technology for enhanced transparency, and the development of predictive analytics as a service (PAaaS). These advancements are expected to make predictive analytics more accessible and efficient. Ethical considerations, such as data privacy, fairness, and environmental impact, are crucial in ensuring responsible use of predictive analytics in SCM. In conclusion, predictive analytics is a strategic tool for modern SCM, offering significant benefits but requiring careful implementation to address challenges and ethical concerns. Organizations that adopt predictive analytics effectively can achieve greater efficiency, resilience, and competitiveness in their supply chain operations.