Artificial intelligence is transforming how scientists study disease at the cellular level. This project focuses on glaucoma, a leading cause of blindness, by analyzing individual cells in the eye to better understand which types are most vulnerable to damage. The retina contains many subtypes of retinal ganglion cells, some of which are more prone to degeneration than others. Identifying these subtypes is crucial for the early diagnosis and treatment of glaucoma; however, traditional methods struggle to handle the volume and complexity of biological data. This research develops an advanced AI model to uncover hidden patterns in massive genetic datasets and identifies distinct subtypes of retinal ganglion cells. These insights could help doctors detect glaucoma earlier and lead to new ways of repairing damaged cells. The work will also give students hands-on experience with real-world health data and help train the next generation of scientists who work at the intersection of computer science and medicine. This project develops a transformer-based machine learning framework for identifying glaucoma-susceptible retinal ganglion cell subtypes using large-scale single-cell sequencing data. Current cell annotation approaches struggle with issues such as marker ambiguity, batch effects, and poor scalability across datasets. The proposed model addresses these challenges by incorporating a patch-based gene embedding strategy and positional encoding to preserve feature structure,