This research paper helps in the analysis of how the inclusion of Artificial Intelligence (AI) and Big Data into supply chain management can be used to make the system much more efficient and sustainable. Artificial intelligence technologies, from machine learning to predictive analytics, are used to run logistics, forecast demand, and manage inventory better. Big Data offers a way to gather insights in real time through various data sources, which leads to better decision-making and responsiveness to the changes in the market. Through machine learning, AI models can sift through massive data sets to identify patterns and predict outcomes that allow supply chain managers to adjust on the fly. The latter is more efficient, with less waste, a smaller carbon footprint, and other resource benefits. And with automation, AI-powered processes can be accelerated, resulting in fewer human errors and operational costs. Introducing big data analytics in firms encourages transparency and traceability, key sustainability factors. The “micro orchestrators” at play behind these revolutions are bred by AI’s face-off at the intersection of Big Data—it has innovations that take place so high up no one even fathoms what is even allowing stuff like dynamic pricing models or personalizing customer experiences by optimizing supply chains for sustainability. However, surmounting these barriers holds great promise for supply chains to become more efficient, resilient, and sustainable, supporting international sustainability targets.