Fundus tessellation (FT) is a recognized early sign of retinal and choroidal remodeling in myopia, associated with axial elongation of the eye. Advances in artificial intelligence, especially deep learning, enable quantitative FT analysis as an objective and scalable tool in myopia research. The review summarizes studies from biomedical databases on the quantification of FT and mosaic cell density (FTD) and their associations with clinical parameters of the eye. Analytical methods include ROI definition, image normalization, and deep learning-supervised segmentation. Some studies report the spatial heterogeneity of FTD and its relationship with peripapillary and macular changes. FT metrics serve as quantitative imaging biomarkers of structural changes in myopia. AI-driven FT quantification increases the objectivity and scalability of retinal images. These approaches support early risk stratification and long-term follow-up of myopia.