<p>Determining an acceptable solution across various factors poses a significant challenge in multiple-criteria group decision-making (MCGDM). Within the healthcare sector, where there is no tolerance for errors and mistakes, selecting a healthcare supplier is one of the most critical areas. Starting with the cardinal data-based methods, the recent approaches to solve such problems have grown to MCGDM methods. Recent advancements have emphasized hybrid MCGDM methods, as hybridized approaches tend to outperform individual methods. In response to this trend, this study describes a fusion of MCGDM and metaheuristic algorithms, as metaheuristics can handle the non-linearity, complexity and uncertainty better than the traditional MCGDM methods. Initially, the study introduces a novel MCGDM optimization model to directly obtain the crisp weights from the fuzzy decision matrices without aggregating the decisions received from multiple experts. Later, an algorithm is developed in the metaheuristic environment, leveraging a hybrid particle swarm optimization (PSO) method to solve the optimization model. By incorporating MCGDM principles into the metaheuristic system, this study enhances the supplier evaluation system of an Indian healthcare facility. The proposed method offers a convenient and robust solution for estimating the weights of suppliers and affecting criteria. The comparison results validate the algorithm. A sensitivity analysis ensures the robustness and efficiency of the method. By integrating the optimization process enables more accurate and reliable decision-making in the supplier selection sector.</p>