Abnormal intraocular pressure within the human eye is one of the main manifestations of glaucoma. In the early stages of the disease, patients often do not experience significant discomfort, making it difficult to be aware in a timely manner. If the condition is not treated promptly, it may lead to complete blindness. Early diagnosis of glaucoma can effectively prevent permanent vision loss. Clinical manual examinations are a viable solution, but they are not only time-consuming and labor-intensive but also require doctors to possess specialized knowledge and experience. Existing research results indicate that integrating artificial intelligence technology into imaging for the prevention and detection of glaucoma is efficient and accurate. This article systematically introduces the latest developments in the field of glaucoma auxiliary diagnosis based on artificial intelligence, discusses various published algorithm models, summarizes the challenges in such research, and outlines possible future research directions. It provides a comprehensive and in-depth review of the current research status and future development trends in intelligent glaucoma detection technology based on multi-modal assessment.