Individuals with auditory impairments face significant challenges in communicating with others in their daily lives, even during simple interactions. These communication barriers can lead to social isolation and limit opportunities for education, employment, and everyday interactions. Our paper aims to address this issue by developing a speech-to-sign language translation system. This system will convert spoken language into sign language, helping bridge the communication gap between deaf and hearing individuals. Currently, solutions that effectively handle sign language translation are limited due to the complexity of gestures, expressions, and context in sign languages, posing a significant barrier for those who rely on it for communication. By addressing this need, our project aims to provide a vital tool for individuals with hearing impairments, making everyday communication more accessible for everyone. Our model has a sequence of steps. The model first converts the audio signal to text. Then, the text is converted to gloss, a simplified written representation of sign language that removes grammatical inflections and spatial grammar. Finally, the gloss is used to extract keypoints that are utilized to render the final video. Our system is tested against multiple state-of-the-art models using various evaluation metrics and across several datasets.