Sign language is a communication tool commonly used by people with hearing impairments or those who are unable to communicate verbally. It utilizes gestures to convey actions and simulate images or syllables that form specific meanings or words. With the continuous development of computer vision and deep learning, sign language recognition technology has emerged and continued to develop, making it possible for hearing individuals to communicate with the deaf or mute. However, the complexity and variability of dynamic sign language still pose challenges for its accurate detection and recognition. To promote research in this field, this study conducts an in-depth review of existing dynamic sign language recognition methods and technologies. First, the development history and current research status of dynamic sign language recognition technology, commonly used dynamic sign language datasets, and evaluation metrics for sign language recognition methods are reviewed. Second, deep learning models frequently used in dynamic sign language recognition are examined, and the challenges faced by dynamic sign language recognition technology, along with corresponding solutions, are discussed. Finally, based on the current status of sign language recognition, the challenges of dynamic sign language recognition are summarized, and an analysis and outlook are provided regarding the potential improvements to sign language recognition performance in the next stage.