<p>Sign language is a vital mode of communication for deaf and speech-impaired people. A sign language recognition system is a system that converts gestures into human-readable text by recognizing signs. In this research paper, we have conducted a comprehensive survey on recent developments in gesture identification systems. There are various approaches in the identification of signs, namely the vision-based approach, sensor-based approach, and data-driven approach. The sensor-based approach relies on wearable devices like gloves, motion sensors, and accelerometers to capture the sign gestures. This may be highly expensive, has limited face and body gestures, and may cause some discomfort. Compared to the sensor-based approach, the vision-based approach is non-intrusive, scalable, and capable of accurately capturing entire body gestures. Despite recent advances in isolated sign recognition, continuous sign identification remains less explored. This highlights the openness in this research area. The review paper encompasses a panoramic outline of current state-of-the-art gesture identification. The survey covers the methodology of vision-based techniques. We have gathered research papers on recognizing signs using deep learning and machine learning published from 2020 to 2025. We have searched research papers using the databases Science Direct, IEEE Xplore, ACM Digital Library, Springer, and Google Scholar. This survey explores the introduction, background, motivation, and challenges related to surveys of the sign language system. The present work explores the challenges associated with the system, including the variability in signing styles, dynamic backgrounds, and the need for high-accuracy recognition and low latency in real-time. This work introduces a classification system for categorizing models to recognize isolated and continuous signs, examining their datasets, hybrid structures, applications, and prospective avenues for upcoming research within this domain. This survey aims to provide experimenters, practitioners, and enthusiasts with an overarching understanding of the current state and prospects of gesture identification, fostering technology innovation to bridge communication gaps and promote inclusivity. This survey provides insights into the current research work, challenges, and promising research opportunities in this domain.</p>