Fundus digital photography and optical coherence tomography (OCT) are currently the primary imaging approaches for early diagnosis and treatment of eye diseases. In recent years, the significant development in artificial intelligence (AI), particularly in machine learning (ML) and deep learning (DL) are new and vital technical-driven motivations impacting on the traditional diagnosis and treatment methods. At the same time, the ultra-wide field (UWF) imaging technology is getting widely accepted and prevalent by its obvious advantageous features of non-dilate pupils, express-track result and the vast pool of fundus viewing angles. As a result, numerous research have been done to explore AI in ultra-wide field fundus imaging ophthalmology for joint diagnosis and treatment. However, the current review of this method is still in least ink. We first outlines the application and impact of AI technology in ophthalmic diseases in the past ten years. With the following part exclusively summarizing the technical integration of ultra-wide field fundus images and AI technology in the past four years, which has brought innovations to clinical treatment methods for the diagnosis and treatment of ophthalmic diseases; finally, we analyzed the application and implementation of the novel technology as well as the potential limitations and challenges, to predict the possibility of the technology's further principles role and values in clinical ophthalmology.