<p>The pandemic outbreak has revealed significant flaws in the complex and highly fragmented Healthcare Supply Chain’s (HSC’s). However, two major issues persist in the HSCs, leading to inefficiencies: transparency in vaccine distribution and accuracy in demand forecasting. The recent pandemic has highlighted and intensified existing vulnerabilities in HSC’s, leading to the effective utilization of digital technologies to manage them. This research proposes a novel framework that merges Blockchain (BC) and Machine Learning (ML) to bolster the HSCs amidst pandemics, by developing a framework named the Predictive BlockVax Distribution Network (PBDN) model. The proposed PBDN model utilizes BC for securing transactions and Long Short-Term Memory (LSTM), for precise demand prediction. Leveraging Hyperledger Besu, which represents an Ethereum client that is accessible for public use, the PBDN framework ensures BC’s privacy, scalability, and efficient network operations, while LSTM’s advanced forecasting outperforms traditional models and Deep Learning (DL) techniques. This integration showcases a significant leap in managing vaccine distribution and enhancing system resilience, fairness, and transparency. The proposed PBDN model illustrates the potential of BC and ML together to tackle pandemic-induced Supply Chains (SC’s) disruptions, providing a decentralized solution that supports autonomous, informed decision-making without third-party dependency. This approach not only addresses immediate challenges but also sets a precedent for future crisis response, emphasizing the need for robust, Transparent Supply Chain’s (TSC’s).</p>