Machine learning, as the main technical direction of artificial intelligence, can help ophthalmologists to interpret and analyze the large amount of data generated by imaging equipment, and also simplify the diagnosis and treatment process.The early diagnosis and classification of keratoconus became the most important application of machine learning.The modeling methods of machine learning for diagnosing keratoconus usually included neural network and decision tree method.The sensitivity and specificity of these models for diagnosing keratoconus were more than 85%.Because there were large number of research parameters for the diagnosis of keratoconus and no adequate public data sets, it was difficult to evaluate the advantages and disadvantages of different research methods, which limited the clinical application of machine learning in the evaluation of keratoconus.The corneal refractive surgery preoperative evaluation had clinical problems of large data volume and difficult decision-making.Machine learning can assist in evaluating whether the patient is suitable for refractive surgery, of which specificity and sensitivity were above 90%.It was also able to predict postoperative visual quality with ocular parameters.In addition, machine learning can also help us to count corneal endothelial cell density and assess corneal epithelial damage.Machine learning method and big data modeling evaluation can assist doctors in accurate diagnosis and personalized evaluation of keratopathy.This article reviewed the recent literature on the application progress of machine learning in corneal-related diseases in recent years.