The emergence of multidrug-resistant (MDR) bacterial pathogens poses a critical threat to global health, demanding intelligent and adaptive predictive systems. This study proposes the application of the Grey Wolf Optimizer (GWO) algorithm as an innovative computational approach for predicting and analyzing multidrug resistance patterns in clinical bacterial isolates. Unlike conventional statistical methods that often fail to handle complex, nonlinear biomedical data, GWO effectively balances exploration and exploitation through swarm intelligence inspired by wolf hierarchy and hunting behavior. A dataset of 10,700 clinical bacterial samples obtained from Kaggle was analyzed, encompassing antibiotic susceptibility profiles and clinical parameters such as patient comorbidities and hospitalization history. The data were normalized and optimized using GWO to identify the most influential attributes contributing to antibiotic resistance. Experimental results demonstrate that GWO achieves strong stability in convergence, efficiently identifying dominant resistance predictors such as CTX/CRO, FOX, and IPM. Compared to traditional optimization methods, GWO offers improved accuracy and robustness in feature weighting and selection. The study concludes that GWO provides a scalable and interpretable framework for multidrug resistance prediction, enabling early identification of critical resistance trends. The implementation of this approach can assist healthcare institutions in formulating more precise antimicrobial stewardship strategies and controlling the spread of resistant pathogens in clinical environments.