Eye fundus conditions are dangerous and can cause significant visual impairment if not detected early. Diabetic retinopathy, cataracts, and glaucoma are among the conditions for which manual assessment is directly impacted by ophthalmologists’ experience. The study intends to use artificial intelligence to develop a diagnostic system that is concentrated on the precise and effective classification of eye fundus diseases in order to address this challenge. The research involves the curation of an extensive dataset, named Eye Diseases Classification, that consists of a variety of eye fundus images that illustrate different conditions, including cataracts, glaucoma, and diabetic retinopathy. Expert ophthalmologists have painstakingly annotated every image in the dataset, offering precise ground-truth information that is essential for segmentation tasks. The findings of the experiment demonstrate how well the suggested AI system performs in accurately classifying eye fundus diseases and recognizing impacted areas in the images. This study could potentially reduce the risk of blindness and severe vision impairment by revolutionizing the diagnosis of these diseases. The system expedites diagnosis by automating the classification process, enabling earlier intervention and treatment. Additionally, using AI lessens the workload for ophthalmologists, freeing up their time for more complicated cases and improving the effectiveness of healthcare as a whole. In the end, using AI to diagnose eye fundus illnesses is a huge step forward that will impact public and clinical health in many ways.