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The integration of Artificial Intelligence (AI) and Machine Learning (ML) in supply chain analytics has emerged as a transformative force in reshaping traditional logistics and operations. This review critically examines the multifaceted role of AI and ML in optimizing supply chain processes, enhancing decision-making capabilities, and fostering agility in an era of dynamic market demands. AI and ML technologies have revolutionized data analytics by enabling the extraction of actionable insights from vast and complex datasets. The application of predictive analytics, powered by machine learning algorithms, allows supply chain professionals to forecast demand more accurately, identify potential disruptions, and optimize inventory levels. This not only improves overall efficiency but also reduces costs and minimizes the risk of stockouts or overstock situations. Furthermore, the integration of AI-driven automation in supply chain management has streamlined routine tasks, such as order processing, inventory replenishment, and route optimization. This automation not only accelerates processes but also mitigates the risk of human errors, enhancing overall reliability. The ability of AI to continuously learn from historical data and adapt to evolving market conditions contributes to a more agile and responsive supply chain ecosystem. In the context of supply chain risk management, AI and ML play a pivotal role in identifying vulnerabilities and providing proactive strategies to mitigate potential disruptions. Sentiment analysis and predictive modeling enable organizations to assess geopolitical, economic, and environmental factors, thereby enhancing the …