Diabetic retinopathy (DR) remains a leading cause of vision impairment worldwide, necessitating early and precise detection to prevent irreversible damage. To this end, the present study introduces an adaptive ensemble learning framework that integrates transfer learning with an Enhanced Adaptive Differential Evolution (EADE) algorithm for robust DR classification. The ensemble comprises three convolutional neural networks—DenseNet121, Xception, and InceptionResNetV2—pre-trained on ImageNet and fine-tuned on retinal fundus images, whose outputs are aggregated via an Enhanced Weighted Voting (EWV) scheme. Ensemble weights are optimized by EADE, which extends the JADE algorithm through adaptive control of the mutation factor and crossover rate and the incorporation of an external archive to preserve population diversity. Evaluation using synthetic performance metrics indicates that the EADE-driven ensemble surpasses static weighting approaches and canonical Differential Evolution in terms of classification accuracy, macro-averaged F \(_1\) -score, and ROC AUC. These findings suggest that the proposed EADE–EWV framework offers a scalable, effective decision-support tool for automated DR screening.