Limiting vision damage and blindness requires timely and precise detection of eye illnesses. Color fundus imaging, a non-invasive and cost-effective modality, provides rich information for detecting various retinal abnormalities. A method that uses deep learning for multiple-class identification of common eye illnesses using color fundus images is proposed in this paper. We utilize DenseNet201 as the backbone architecture due to its efficiency in feature reuse and gradient flow. The suggested approach underwent typical preliminary processing processes, such as high pass filtering and data augmentation to improve predictive abilities, and was trained and assessed using a carefully selected labeled dataset of high-quality color fundus photographs. The experiments’ findings demonstrate that the suggested structure outperforms the standard models and exhibits strong generalization capability with an accuracy of 89.61%, making it a promising tool for automated screening in clinical settings. The outcomes of this research reveal that the integration of a densely connected deep neural network with a systematic hyperparameter tuning significantly improves multiclass retinal disease classification, providing a viable solution for deployment in real-time clinical diagnostic systems.