Aims: 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, augmenting clinical decision-making and facilitating timely intervention. However, its clinical efficacy compared to standard assessment is inadequately understood. This study aims to examine the clinical impact of artificial intelligence algorithms and telemedicine 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 acceptability, satisfaction and engagement were included. Editorials, non-clinical studies and literature reviews were excluded. Results: The database search identified 7150 articles. To date, we have screened 640 articles, and after full-text retrieval, 15 studies were included in this systematic review. Most applications of AI and telemedicine in otology included optimisation of hearing aid technology, enhancing imaging modalities and management of vestibular disorders. There were 3 studies assessing patient outcomes, which indicated high levels of positive 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. Further research should focus on optimising the user interface across diverse clinical settings.