Herath H.M.S.CSwarnajith T.H.M.PSenanayaka W.S.H.MHettiarachchi H.W.R.AWalgampaya, NDe Zoysa, R2026-10-032025-12-09H. H. M. S. C., S. T. H. M. P., S. W.S.H.M., H. H.W.R.A., N. Walgampaya and R. De Zoysa, "Eye Health Monitoring and Eye Care System," 2025 7th International Conference on Advancements in Computing (ICAC), Colombo, Sri Lanka, 2025, pp. 1-6, doi: 10.1109/ICAC69156.2025.11361466.979-833156222-9https://rda.sliit.lk/handle/123456789/5312This study presents a multi-modal AI-powered system designed for real-time eye health monitoring and early diagnosis. The proposed system integrates computer vision, machine learning, and image processing techniques to deliver non-invasive and accessible diagnostic solutions. The system comprises four key modules. The first one is a real-time eye exercise module that utilizes webcam-based eye tracking and adaptive machine learning to provide personalized routines for reducing digital eye fatigue. The second module is an AI-driven cataract detection module that analyzes retinal images to enable early diagnosis, particularly in resource-limited settings. The third module is a glaucoma detection system that tracks pupil dynamics, such as size and light reactivity, to identify early symptoms; and the fourth and final module is a color blindness and eye fatigue detection module that employs multi-modal AI techniques to assess color vision deficiencies and monitor signs of visual fatigue in real-time Collectively, these components form a comprehensive array of tools focused on improving eye health monitoring and treatment. Through the combination of advanced AI technologies with user-friendly interfaces, the proposed systems aim to democratize access to eye care, reduce the global burden of preventable vision loss, and enhance the quality of life for individuals everywhere.enComputer visionEye health monitoringMachine learningNon-invasive eye disease detectionReal-time eye fatigue analysisConference Paperdoi: 10.1109/ICAC69156.2025.11361466.