Vision problems, particularly retinal diseases, have become increasingly prevalent in recent times. Conditions such as diabetic retinopathy, glaucoma, and cataract are among the leading causes of vision loss worldwide. The burden is especially severe in developing and low-income countries, where access to specialized eye care remains limited. Early detection and accurate classification are vital to prevent irreversible damage and improve patient outcomes. This study introduces a hybrid retinal diseases classification model using convolutional neural networks and fundus images that combines Convolutional Neural Networks with advanced image preprocessing and augmentation techniques to analyze retinal fundus images. The model was trained and validated on both publicly available datasets and real clinical data, ensuring reliability and adaptability across diverse settings. Preprocessing steps such as normalization, noise reduction, and image enhancement improved image quality, while augmentation techniques helped address class imbalance. Experimental results showed strong diagnostic performance, with the model achieving 92.75% accuracy, 88.89% recall, 92.02% specificity, 88.89% precision, and a 90.57% F1-score. These results highlight the system’s ability to minimize errors and deliver consistent outcomes. The model provides a scalable, efficient, and clinically relevant tool to support ophthalmologists in early disease detection, particularly in underserved communities, offering a promising step toward reducing preventable blindness.