This article discusses how the development of machine learning (ML) methods have advanced biomarker discovery, cell type classification, and ocular disease modeling. This is a retrospective study of studies conducted from 2019 to 2025 which synthesizes AI-enabled transcriptomic studies which provides insight into the development of the retina and corneal disease. Techniques such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and weighted gene co-expression network in analysis (WGCNA) are the standard in single cell workflows with the use of AI. Supervised learning such as least absolute shrinkage and selection operator (LASS), support vector machine (SVM), and random forests (RF) can aid in discovering biomarkers which can predict if a patient has or may develop a disease. In advanced neural networks, there are variational autoencoders which can combine multi-omics data to build disease models and predict outcomes more accurately. That being said, it is difficult to interpret how AI makes its predictions and there is a lack of standardization of results among all of the studies included. Despite these challenges, there is the possibility of explainable AI and multimodal approaches to contribute to more personalized and precise treatment in ophthalmology.