This chapter explores the use cases and applications of AI and ML in optimizing supply chainSupply chain processes, enabling efficiency and agility across interconnected workflows. It discusses the use cases of ML in demand forecasting, contrasting it with traditional methods limited by rigidity and scale. The chapter includes discussion on AI-driven inventory optimization, which utilizes advanced algorithms to refine stock levels, placement, and replenishment strategies. Dynamic replenishment frameworks connect real-time consumption signals to automated production, mitigating risks of stockouts or overstocking. AI’s application use cases in logistics are discussed for route planning, delay prediction, and contingency planning. Models such as LSTM networks improve traffic forecasting, while reinforcement learning optimizes delivery routes and schedules. Predictive analytics enable businesses to anticipate logistics delays and adapt proactively. The chapter includes detailed discussion on anomaly detection, root cause analysis, and KPI monitoring, showcasing AI’s ability to identify disruptions and enhance decision-making. In addition, the chapter explores generative AI’s potential in procurement, automating data-intensive tasks and refining strategic operations. Real-life implementations, including predictive cargo management and risk mitigation in logistics, demonstrate AI’s practical impact. This chapter provides a comprehensive view of how AI and ML reshape supply chainSupply chain resilience and performance.