This review article explores the applications and challenges of neural networks (NNs) in otolaryngology (ENT). It discusses the types of NNs available, their advantages, disadvantages, and practical applications. The review highlights how NNs, particularly convolutional NNs (CNNs), have been used to assist in diagnosis, treatment management, image enhancement, voice analysis, hearing prediction, and head and neck surgery. Despite the promising results, the lack of standardization in AI protocols and data homogeneity remains a significant challenge. The article emphasizes the importance of understanding the basics and types of NNs to develop more effective AI tools for medical applications. The review also addresses the ethical considerations and the need for multicenter collaboration to achieve optimal results in the future.This review article explores the applications and challenges of neural networks (NNs) in otolaryngology (ENT). It discusses the types of NNs available, their advantages, disadvantages, and practical applications. The review highlights how NNs, particularly convolutional NNs (CNNs), have been used to assist in diagnosis, treatment management, image enhancement, voice analysis, hearing prediction, and head and neck surgery. Despite the promising results, the lack of standardization in AI protocols and data homogeneity remains a significant challenge. The article emphasizes the importance of understanding the basics and types of NNs to develop more effective AI tools for medical applications. The review also addresses the ethical considerations and the need for multicenter collaboration to achieve optimal results in the future.