The pharmaceutical sector is part of a complex and heavily regulated global supply chain, in which the provision of essential medicines and therapeutic products on a timely and consistent basis is crucial for public health and clinical results. Traditional pharmaceutical supply chain models have nonetheless been demonstrated to have systemic deficiencies, particularly in the context of escalation events such as
the coronavirus disease 2019 (COVID-19) pandemic, geopolitical turmoil, limited access to raw materials, cybersecurity attacks, and climate-induced incidents. These challenges have expanded the importance of a new kind of digital supply chain, one that is more dynamic, data-driven, and less static – one that can anticipate disruption and act proactively. The role of this paper, therefore, is to suggest an integrated strategy development framework for a future-ready pharmaceutical supply chain, one that is predictive, resilient, sustainable, secure, and digitally intelligent. This study begins with a review of the existing literature, which highlights several significant trends in the pharmaceutical supply chain over the past decade, including digital twin modeling, blockchain traceability, machine learning-based demand sensing, and decentralized manufacturing. The survey includes cases of multinational pharmaceutical companies and data from regulatory agencies, such as the US FDA and the EMA, to identify existing gaps and potential solutions. Adopting a mixed-methods approach, the study's findings, based on qualitative analysis (expert interviews and thematic synthesis), are complemented by quantitative analysis of metrics (frequency of supply disruptions, variability in lead times, and production post-recovery rates) to unveil strategic dimensions for modernization. Key strategies identified include the application of AI-enabled supply chain control towers, creating digital twins of the manufacturing and logistics environments for predictive simulation, deploying blockchain to track provenance and compliance in real-time, and adopting decentralized manufacturing to onshore production and decouple from overdependent global intermediaries. The study also emphasizes the significance of ecosystem partnerships among pharmaceutical companies, regulators, third-party logistics (3PL) companies, and digital infrastructure providers in ensuring interoperability and confidence in the system. Risk stratification models and data-driven prioritization matrices are presented as decision support tools to optimize supplier portfolios and manage inventory buffers in uncertain times.The findings of this study show that drug supply chains developed to be predictive, analytic, automated, and end-to-end exhibit significantly improved resilience and responsiveness when shocks occur, as well as better regulatory compliance. For example, firms that utilized an RNN-driven demand forecast and a weather disruption dataset were able to mitigate product stockouts by 47% during a significant logistics hold. In addition, pilot program deployments of blockchain for vaccine traceability cut down counterfeit cases by over 70%, demonstrating the value that DLT can deliver in the operations arena. This paper serves as a supplementary contribution to the emerging literature on pharmaceutical supply chain logistics, offering a visionary roadmap based on technological breakthroughs, regulatory responsiveness, and strategic risk management. The findings reinforce the stance that future-readiness is not just about infrastructure renewal, but also about developing flexibility in operational processes,fostering coherent collaboration across functions, and cultivating a culture of continuous learning within supply chain ecosystems. These learnings are designed to inform pharmaceutical manufacturers, healthcare systems, policymakers, and supply chain architects as they develop the capabilities required for future Precision Medicine, rapid therapeutic deployment, and global health equity.