This chapter offers a detailed overview of the main artificial intelligence (AI) algorithms shaping ophthalmic diagnostics, treatment improvement, and research progress. It discusses the core design and clinical uses of key AI models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), support vector machines (SVMs), generative adversarial networks (GANs), reinforcement learning (RL), and various regression and classification methods. The chapter also highlights ensemble methods such as decision trees, random forests, K-means clustering, and K-nearest neighbor algorithms, focusing on their ability to improve diagnostic accuracy, categorize disease risk, and enable personalized ophthalmic treatment. By incorporating these algorithmic approaches into imaging, prediction, and treatment processes, ophthalmology is entering a new phase of precision medicine fueled by AI innovation and experimentation.