Background and objective: The unique organ that provides vision is eye and there are various disorders cause visual impairment. Therefore, the identification of eye diseases in early period is significant to take necessary precautions. Convolutional Neural Network (CNN), successfully used in various imageanalysis problems due to its automatic data-dependent feature learning ability, can be employed with ensemble learning. Methods: A novel approach that combines CNNs with the robustness of ensemble learning to classify eye diseases was designed. From a comprehensive evaluation of fifteen pre-trained CNN models on the Eye Disease Dataset (EDD), three models that exhibited the best classification performance were identified. Instead of employing traditional ensemble methods, these CNN models were integrated using a weighted-voting mechanism, where the contribution of each model was determined based on ABC (Artificial Bee Colony). The core innovation lies in our utilization of the ABC algorithm, a departure from conventional methods, to meticulously derive these optimal weights. This unique integration and optimization process culminates in ABCEnsemble, designed to offer enhanced predictive accuracy and generalization in eye disease classification. Results: To apply weighted-voting and determine the optimized-weights of the best-performing three CNN models, various optimization methods were analyzed. Average values for performance evaluation metrics were obtained with ABCEnsemble as accuracy 98.84%, precision 98.90%, recall 98.84%, and f1-score 98.85% applied to EDD. Conclusions: The eye diseases classification success of 93.17% obtained with DenseNet169 was increased to 98.84% by ABCEnsemble. The design of ABCEnsemble and the experimental findings of the proposed approach provide significant contributions to the related literature.