Fundus image-based artificial intelligence (AI) technology has shown substantial promise for myopia detection. AI models can directly infer refractive status and axial length from color fundus photographs, and multi-task learning strategies may further improve the accuracy of joint prediction. For myopia-related lesion recognition, enhanced U-Net architectures and Transformer-based models have achieved near-expert performance-and in some tasks even surpassed expert graders-in detecting and segmenting findings such as peripapillary atrophy, choroidal thickness-related changes, and tessellated fundus. Currently, automated grading under the Meta-analysis for pathologic myopia (META-PM) classification framework is also becoming increasingly mature. In parallel, optical coherence tomography (OCT) demonstrates clear advantages in identifying tractional and neovascular macular complications. In terms of risk prediction, models that integrate color fundus photography with baseline clinical information-including refractive error, axial length, and age can more accurately estimate the risk of progression to high myopia in children, thereby supporting screening and individualized management. Nevertheless, insufficient generalizability remains a major barrier, driven by heterogeneity in data sources, inter-device variability, and coexisting ocular conditions. In the near future, rapid advances in multimodal large-scale data fusion, interpretability and uncertainty quantification, and device-agnostic platforms are expected to accelerate end-to-end intelligent management of pathologic myopia.