The resilience of the pharmaceutical supply chain (PSC) is crucial to ensure the continuous availability of essential medicines and other products, particularly during logistics disruptions. This article presents a systematic review of the literature on machine learning (ML) and artificial intelligence (AI) techniques and their importance to PSC resilience. By analyzing existing literature from multiple databases, including Web of Science and IEEE Xplore, over the past five years, we identified key areas where ML and AI have been effectively utilized. These areas include demand forecasting, risk management, and inventory optimization. This review also highlights significant research gaps and proposes future directions for investigation. Our findings suggest that while ML and AI offer promising solutions for improving supply chain resilience, there is a need for more studies that integrate various ML and AI approaches into PSC. Our analysis reveals that there are no clear government regulations related to the usage of ML or AI in PSC, no robust real-world applications addressing the challenges of adopting these technologies, and no clear predictive models to assess their impact on PSC resilience. This ongoing work aims to provide a foundation for future research, ultimately fostering more adaptive and resilient PSCs.