The most common causes of vision loss in people worldwide are cataract, glaucoma, and retinal disorders. The rising prevalence of these diseases necessitates an immediate, accurate diagnosis. The suggested approach is created and designed to make it simple for individuals to diagnose illnesses of the retina, glaucoma, cataract and many more. Artificial neural networks and convolutional neural networks are used to classify and locate eye problems. The suggested approach will reduce the amount of brought-on blindness by enabling patients to receive the necessary care for the mentioned illnesses at an early stage. The chosen method also evaluates the effectiveness and safety of cataract surgery in eyes with age-related macular degeneration in addition to identifying glaucoma and retinal diseases. This study uses photos of the fundus from healthy eyes as well as eyes with glaucoma, cataracts, and retina to show the accuracy of algorithms. Nowadays, the concept of categorizing photographs based on their fundus and extracting features is well recognized, and it also plays a crucial role in the conclusion.