Globally, retinal disease represents a considerable risk to vision health, emphasizing the critical need for current strategies to ensure effective treatment. Recently, deep learning methods have shown promise in automating the detection and diagnosis of retinal diseases from medical images. The paper explores the pre-trained VGG16 convolutional neural network (CNN) architecture, originally trained on the ImageNet dataset, for categorizing eye disease from fundus images. After calculating the working of the VGG16 model in distinguishing between healthy and diseased retinas and analyzing those results with another deep learning design for medical figures or image examination used in general [8]. Our findings demonstrate the strength of the VGG16 model in accurately identifying retinal diseases, highlighting its potential as a helpful appliance for early disease classification and scientific decision support [15].