Artificial intelligence-based pelvic floor ultrasound helps the diagnosis, preoperative assessment, and postoperative monitoring of female pelvic floor dysfunction (FPFD). The application of artificial intelligence in pelvic floor ultrasound mainly includes automatic segmentation and measurement, the diagnosis of muscle injury, childbirth prediction and postoperational evaluation. It can not only overcome the problem of operator experience dependence but also improve work efficiency and simplify the workflow, which has popularized the application of pelvic floor ultrasound. However, most of the current research is still limited to the automatic segmentation of three-dimensional axial plane levator hiatus (LH). The automatic reconstruction, real-time tracking of 3D/4D images and the imaging navigation of pelvic floor surgery remain major challenges for researchers.