Early screening for Marfan syndrome (MFS) is crucial in reducing related mortality rates. The current clinical diagnosis of MFS is mainly based on the Ghent criteria revised in 2010, with core features of lens ectopia (EL) and aortic root dilation. Modern ophthalmic imaging technology can help discover deep biomarker spectra beyond EL in MFS, such as axial elongation and reduced total corneal refractive power; the corneal curvature value decreases, the central corneal thickness becomes thinner, and the corneal hardness increases; thinning of the ciliary body, decreased thickness of the sclera, and increased asymmetry; decreased retinal microvascular density, thinning of retinal nerve fiber layer, and increased risk of retinal detachment; changes in choroidal structure and function abnormalities, as well as facial features. The ophthalmic imaging omics model based on artificial intelligence integrates multimodal imaging data such as eye images and biometric measurements to construct high-precision prediction tools, could significantly improve sensitivity and accuracy in MFS screening. This strategy not only facilitates early screening of MFS, but also enables dynamic monitoring of disease progression, assists in guiding personalized interventions for cardiovascular disease, and ultimately optimizes patient lifecycle management.