Iris damage prediction plays an important role in ophthalmology, assisting in the identification and prevention of eye impairments. This paper examines a deep learning–based approach using Convolutional Neural Networks (CNN) and advanced image processing techniques to improve the accuracy of iris damage classification. The image is qualified for improved iris data recording, exploiting attribute extraction and enhancement approaches to attain accuracy in identifying irregularities. Essential elements of this study depend on the work of Casia et al. Provide notably to iris feature extraction using machine learning; our system combines data enrichment, reduced noise and difference enhancement strategies to enhance the strength of the image. Investigation findings illustrate a significant increase in prediction accuracy contrasted with conventional methods, emphasizing the effectiveness of CNN in medical imaging applications. The results of this study have implications for automated ophthalmic features, facilitating live iris health assessment and preventive measures.