<p>This shows that personalized ranking is crucial to efficient supplier selection for sustainability because it includes social, economic, and environmental attributes in the decision-making process. To provide a solution for an efficient evaluation of suppliers, this study introduces the Equivariant. White shark Quantum Neural Network (EWSQNN) model for getting a personalized ranking of the sustainable supplier selection where weight settings are different and consider individual preferences. This system processes data from the Sustainable Supplier Selection Dataset using LGDN for preprocessing and IT for feature extraction. The Prairie Dog Optimization Algorithm (PDOA) takes this process one-step further and concentrates on identifying the most critical features. The EWSQNN enhances decision support because of the equivarient and quantum network features that enable the handling of conflict and uncertainty. The methodology used is comprehensive and generates a ranking of suppliers based on sustainability performance tailored for an organization by means of multi-criteria decision-making techniques.</p>