In this study, we present an novel approach to enhance the predictive performance of ensemble-based machine learning models for early disease diagnosis. We introduce a novel ensemble model incorporating Grey Wolf Optimization (GWO) based feature selection and a newly designed fitness function emphasizing specificity and sensitivity. The effectiveness of our proposed model is validated using five disease datasets from the UCI machine learning repository: Chronic Kidney Disease (CKD), Statlog Heart Disease (SHD), Cleveland Heart Disease (CHD), Pima Indian Diabetes (PID), and Wisconsin Breast Cancer (WBC). Our proposed model surpasses State-of-the-Art (SOTA) ensemble and non-ensemble models in terms of Accuracy, Sensitivity, Specificity, and AUC. Additionally, a Paired T-Test with 95% confidence confirms the significant superiority of our model over previous base and ensemble models. This research showcases a promising step forward in leveraging machine learning for accurate and early disease diagnosis.