Aging is a major risk factor for various eye diseases, such as cataract, glaucoma, and age-related macular degeneration. Age-related changes are observed in almost all structures of the human eye. Considerable individual variations exist within a group of similarly aged individuals, indicating the need for more informative biomarkers for assessing the aging of the eyes. The morphology of the ocular anterior segment has been reported to vary across age groups, focusing on only a few corneal parameters, such as keratometry and thickness of the cornea, which could not provide accurate estimation of age. Thus, the association between eye aging and the morphology of the anterior segment remains elusive. In this study, we aimed to develop a predictive model of age based on a large number of anterior segment morphology-related features, measured via the high-resolution ocular anterior segment analysis system (Pentacam). This approach allows for an integrated assessment of age-related changes in corneal morphology, and the identification of important morphological features associated with different eye aging patterns. Three machine learning methods (neural networks, Lasso regression and extreme gradient boosting) were employed to build predictive models using 276 anterior segment features of 63,753 participants from 10 ophthalmic centers in 10 different cities of China. The best performing age prediction model achieved a median absolute error of 2.80 years and a mean absolute error of 3.89 years in the validation set. An external cohort of 100 volunteers was used to test the performance of the prediction model. The developed neural network model achieved a median absolute error of 3.03 years and a mean absolute error of 3.40 years in the external cohort. In summary, our study revealed that the anterior segment morphology of the human eye may be an informative and non-invasive indicator of eye aging. This could prompt doctors to focus on age-related medical interventions on ocular health.