<p>Chinese Sign Language is a language used by deaf individuals who communicate in Chinese-speaking communities across various countries. Technology has helped bridge the gap between deaf people and society through communication systems. This research aims to search, collect, and map research efforts related to these technologies, classify the literature into a coherent taxonomy, identify key elements of this field, and propose future research directions in this domain. To achieve this goal, a systematic review was conducted on Chinese Sign Language Recognition (CSLR) using three major databases: Web of Science, ScienceDirect, and IEEE Xplore. The reason for selecting these databases is to cover relevant technical literature related to the topic. The final set consisted of articles from these sources focusing on CSLR technologies. Most of the studied research focus on camera-based CSLR design, which was further divided into video-based systems, Kinect-based systems, and Leap Motion camera-based systems. Another group examined sensor-based approaches for CSLR, split into sensory glove methods and bio-signal (brain, nerve, and muscle) acquisition techniques. A smaller category included other types of research, such as machine learning, feature extraction and evaluation, review articles, and linguistic studies integrating Chinese Sign Language with animation technologies. Although several research articles address the topic of Chinese Sign Language, they lack a systematic approach. For example, many studies depend on small datasets for different data collection systems or focus only on recognition methods without tackling technical challenges. Research into linguistic aspects within data collection systems remains relatively scarce. CSLR is a rich and diverse research field with significant challenges and opportunities. This review helps clarify the available options and limitations, thereby pointing out directions for future research in this area.</p>