Ensemble learning becomes a backbone in disease diagnosis using several classifiers to ensure improved prediction accuracy and also model reliability. However, conventional ensemble techniques often suffer some critical challenges, like poor diversity among base models, less efficient convergence, and sometimes high computational costs. That is why addressing these matters is essential to make further strides in ensemble-based diagnostic frameworks. This study introduces the Adaptive Ensemble Optimization with Hybrid Learning (AE HL) as an Novel Bagging Approach with Teaching-Learning-Based Optimization (BA-TLBO). The AE-HL framework encompasses a new fitness function that uses a new diversity metric with the Hamming distance to optimize both accuracy and classifier diversity effectively. To counteract inefficiencies in convergence, AE-HL uses adaptive optimization strategy that learns to balance exploration and exploitation during the learning phase. A multi-phase An optimization technique is employed, that limits the amount of computation by successively refining the best promising configurations; dynamic bag size adaptations improve the trade-off between variance and bias and, hence generalization over different datasets. Furthermore, the approach is integrated with a lightweight Explainable AI (XAI) module in order to support interpretability without an increase in complexity. The method is tested on several benchmark datasets for disease diagnosis where it is shown that AE-HL outperformed best among several ensemble optimization techniques. In summary, the proposed method obtained the highest accuracy with explainability and diversity in comparison with advanced metrics and statistical analysis. These results confirm the robustness, efficiency, and transparency of the AE-HL as a solution for enhancing systems for disease diagnosis.