The integration of artificial intelligence (AI) and machine learning (ML) in supply chain management (SCM) represents a critical shift toward data-driven decision-making and operational efficiency in global markets. This paper investigates the specific applications of ML techniques, including regression models for precise demand forecasting, clustering algorithms for supplier segmentation, and reinforcement learning for optimizing logistics. These methods are critically analyzed for their ability to enhance supply chain adaptability and resilience, particularly in volatile and uncertain environments. Our research further explores how ML complements broader technological advancements, including AI, the Internet of Things (IoT), and blockchain, drawing connections between these innovations and their cumulative impact on SCM. Through real-world examples, this paper identifies the measurable benefits of ML, such as cost reductions, improved sustainability metrics, and enhanced decision-making capabilities, while also addressing key barriers to implementation. We critically examine challenges such as data fragmentation, the demand for specialized expertise, and scalability constraints to highlight areas requiring strategic intervention for enhancing the momentum in the overall transition to next-generation technologies in SCM. The analysis concludes with insights underscoring the pivotal role of ML in shaping future-ready supply chains that balance efficiency with sustainability in a complex global landscape.