This study utilizes a systematic review approach to understand the role of Artificial Intelligence (AI) in the diagnosis and prognosis prediction of ophthalmological and systemic diseases. In the field of ophthalmology, AI has demonstrated the ability to detect diabetic neuropathy, glaucoma, age-related macular degeneration, retinopathy of prematurity, pathological myopia, and identify myopia-related complications. It has also been utilised in identifying anterior segment eye diseases, including microbial keratitis, keratoconus, dry eye syndrome, and Fuchs endothelial dystrophy. Additionally, AI has been used in the diagnosis, prognosis, and surgical planning of cataract surgery complications, as well as in the detection of papilledema, pseudopapilledema, ocular myasthenia gravis, strabismus, and the localization of central nervous system lesions through the analysis of extraocular movements and nystagmus. Furthermore, AI can extract features from Optical Coherence Tomography (OCT) images, which when combined with automatic retinal image analysis, can predict autism spectrum disorder by identifying variations in the retinal nerve fiber layer. Additionally, machine learning models analyzing OCT images have also shown potential in detecting anemia in children. Moreover, AI shows promise in identifying hepatobiliary diseases by analyzing the conjunctiva using deep learning models applied to color fundus photography and slit-lamp external eye images. As AI advances, it will support doctors and patients with diagnoses, prognoses, and treatment recommendations, though addressing factors such as ethnicity, race, and gender will remain a challenge.