The COVID-19 pandemic profoundly disrupted global supply chains, exposing vulnerabilities in traditional logistics, procurement, production planning, and distribution mechanisms. As organizations grappled with unforeseen demand shocks, manufacturing halts, and logistics bottlenecks, the importance of building resilient supply chains capable of responding dynamically to disruptions became unequivocal. Artificial Intelligence (AI)—encompassing machine learning (ML), predictive analytics, natural language processing (NLP), computer vision, and autonomous agents—has emerged as a transformative enabler in enhancing supply chain resilience in the post-pandemic era. This research article investigates the role of AI in augmenting supply chain resilience across key functions such as demand forecasting, inventory optimization, risk management, supplier relationship management, and logistics operations. It provides a critical review of the literature, presents empirical findings, discusses challenges and ethical considerations, and offers actionable insights for practitioners and researchers. The results establish that AI not only enables greater operational agility but also contributes to proactive risk mitigation, improved decision-making, and strategic flexibility. However, barriers such as data quality, integration complexities, and workforce readiness remain pertinent. The article concludes with recommendations for leveraging AI responsibly to construct future-ready supply chains. To maximize AI’s potential in supply chain resilience, organizations must prioritize data governance, invest in scalable infrastructure, and foster cross-functional collaboration. Emphasizing ethical AI deployment ensures transparency, accountability, and fairness in automated decision-making processes. Future research should focus on developing adaptive AI frameworks that can evolve with changing market dynamics and emerging risks.