Abstract: Sign Language Recognition (SLR) is a significant area of research aimed at bridging communication gaps between native sign language users and non-native speakers. This paper presents a study on the development and implementation of a novel system using advanced machine learning techniques. Our proposed system utilizes deep learning to interpret hand gestures accurately and convert them into text or speech in real-time. We explore various aspects of SLR, including gesture recognition, hand shape recognition, gesture measurement, and translation, to enable efficient and reliable translation across various datasets. Experimental results demonstrate the effectiveness and efficiency of our system in recognizing a variety of gestures with high accuracy and efficiency. Furthermore, we discuss the benefits of our research in providing interactive communication solutions for people who are deaf or hard of hearing, as well as the potential for integrating technology into communication devices. This research contributes to sign language awareness and underscores the importance of technology in promoting accessibility and community inclusion.