Purpose: The intersection of artificial intelligence (AI) and telemedicine represents a transformative frontier in otology, enabling rapid, remote diagnostics of ear disease based on otoscopic images, audiograms and tympanograms. This is particularly important for rural and remote communities, including Aboriginal and Torres Strait Islander populations, where access to specialised otology services is limited. Despite this potential, its clinical efficacy compared to standard assessment is inadequately understood. This study aims to examine the clinical impact of artificial intelligence algorithms in otology. Methodology: Using PRISMA guidelines, a systematic review was conducted on seven online databases for articles that used artificial intelligence algorithms in otology. English language studies with primary or secondary endpoints pertaining to diagnostic efficiency, accuracy, or patient or provider satisfaction and engagement were included. Results: The database search identified 2550 articles. To date, we have screened 950 articles, and after full-text retrieval, 15 studies were included in this systematic review. Most applications of AI in otology included enhancing imaging modalities and management of vestibular disorders. Three studies assessed patient outcomes, indicating high levels of engagement and patient satisfaction, with 70% of healthcare practitioners expressing positive engagement. Diagnostic or prognostic ability of the AI algorithms was evaluated in 8 studies, achieving overall accuracy of 91%. Conclusion: AI-assisted telemedicine shows strong potential to enhance otologic care by improving diagnostic accuracy, streamline management, and expand access to healthcare, especially in underserved areas.