Supplier selection in healthcare systems, particularly under crisis conditions such as war, economic sanctions, and pandemics, is a highly sensitive and complex issue. Any disruption in the supply of critical equipment, such as dialysis machines, can directly impact the quality of healthcare services and patient safety. With the advent of Industry 4.0 and the proliferation of digital technologies, criteria related to digitalization and smart capabilities have become essential dimensions in supplier evaluation. However, few studies have addressed supplier selection using an integrated approach that encompasses green, agile, and Industry 4.0 dimensions under uncertainty. This research proposes a stochastic decision-making model for selecting medical equipment suppliers, simultaneously considering greenness, agility, and Industry 4.0 aspects under three scenarios: pessimistic, probable, and optimistic. To achieve this, criterion weights were first calculated using the Stochastic Best-Worst Method (BWM), followed by ranking the suppliers via the Stochastic TOPSIS method. The weighting results indicated that “quality,” “service level,” and “robustness” hold the highest priority under crisis conditions. Furthermore, the supplier ranking results demonstrated the superiority of the first selected supplier. Comparing the results with Stochastic VIKOR and Stochastic COPRAS methods confirmed the high convergence and robustness of the proposed approach. The findings of this study can serve as a practical guide for healthcare managers in designing resilient and agile supplier evaluation and selection strategies to navigate crises effectively.