Abstract: This systematic study investigates the latest developments in neural network technology for effectively understanding hand motions in sign language. It is based on a thorough investigation of 100 academic articles published between 2013 and 2020. Sign language recognition is essential for enabling effective communication and accessibility for those who have hearing problems. In the last ten years, many research articles have presented several neural network models that aim to accurately identify sign language motions. These models have been trained on various datasets, including the American Sign Language (ASL) alphabet and others. The main goal of this study is to conduct a comprehensive evaluation of the effectiveness of current neural network methods in understanding hand movements in sign language. This will be achieved by synthesizing information from reliable and reputable sources in the field. This paper provides a thorough analysis of model architectures, training datasets, assessment measures, and obstacles to provide a complete understanding of the current cutting-edge methodologies. It also highlights possible directions for future research. This research seeks to contribute to the growth of technology-driven solutions for improving communication accessibility and inclusiveness for those with hearing impairments by carefully examining the strengths and limits of neural network-based sign language interpretation systems.