During an emergency, there are many activities in a pharmaceutical supply chain (PSC) which are rendered ineffective. This study aims to propose a holistic approach for Big Data Driven Pharmaceuticals Supply Chain (BDDPSC) during medical emergencies. During unprecedented situations, the quality of healthcare services by using traditional supply chain becomes a challenge. The current study aims to generate a model wherein multiple entities will be the part of data entries like hospitals, clinical trials, medical practitioners, and drug manufacturing companies during COVID-19. The study has considered certified medical practitioners as the experts and based their responses for proposing a theoretical framework deploying E-Delphi–Qualitative Data Analysis approach. By critically examining experts’ responses and comments, the study formulated four major themes and ten sub-themes for smooth functioning of BDDPSCs during an emergency. The E-Delphi was conducted in two rounds to reach final consensus and to find a balance for PSC in terms of efficiency and quality. This research is novel wherein big data enabled PSC theoretical model has been formulated using a qualitative approach for handling COVID-19. The proposed framework provides an enriched way to capture data from the important link viz. “health officials” of PSCs.