Endolymphatic hydrops (EH) serves as a crucial pathological basis for various vestibular disorders and holds particular significance in the diagnosis of Meniere's disease. With the continuous advancement of magnetic resonance imaging (MRI) technology, especially the application of delayed gadolinium-enhanced delayed 3D-FLAIR sequences, the capability for imaging diagnosis of EH has been significantly enhanced. Clinically, the interpretation of inner ear hydrops imaging often relies on experienced experts; however, this process is highly subjective and time-consuming, posing a substantial challenge to the precise diagnosis of peripheral vestibular disorders. In recent years, the application of artificial intelligence (AI), particularly deep learning-based image analysis methods, has provided a novel solution for the rapid and objective assessment of EH. This article reviews the fundamental principles of radiomics and AI in EH diagnosis, the current status of their application in interpreting inner ear hydrops imaging, existing challenges, and future development directions. It aims to provide theoretical support and technical reference for the precise diagnosis and personalized treatment of inner ear diseases.