The majority of community in globe use sign language as the most basic way of interaction with Deaf and speech-impaired people. In most instances, a person finds it difficult to learn sign language for communicating with deaf and dump people, which leads to isolation among those individuals. Most people are unaware of the interpretations made in sign language. Hence, this paper presents an intelligent sign recognition system for translation of dynamic sign language for easy communication among people with hearing and speech impairments. The intelligent system takes advantage of advanced computer vision and deep learning techniques to identify dynamic hand signs accurately. This approach includes video data capture, preprocessing, feature extraction, and real-time gesture recognition. Hand movements are captured from webcam video streams, and the MediaPipe library is used to capture key points over the hand. A sequential model based on deep learning maps the relationships in hand gestures, which ensures high recognition accuracy. Extensive testing on different hand gesture recognition datasets shows that they perform efficiently and reliably in real-world situations. This technology facilitates greater accessibility through the ability to quickly and accurately translate sign language, thereby helping create inclusive communication technologies.