One of the main ways of communication for those who have speech and hearing impairments is sign language. Moreover, it remains a challenge to establish mutual comprehension—connecting sign language consumers with those who do not understand its gestures. To improve interactions between people, this study introduces an instantaneous gesture interpretation system that converts hand gestures into text and audible form. To capture movement details, the solution combines hand-positioned tracking mechanisms with pattern recognition models, especially those that analyze visual data sequences. In short, the framework uses layered learning architectures for processing motion data that has been captured, and then sequential data analysis is used to categorize gestures. The migrated text outputs feeding into voice generation modules make sign language conversion possible. These elements are bundled through modular programming into a browser-accessible interface that provides visual feedback displays and clickable controls. Motion interpretation accuracy was performed consistently during testing phases under various hand speeds and lighting conditions. Regardless of periodic lag in evaluating complex finger configurations, early trials with limited training samples showed satisfactory recognition rates, indicating potential for practical deployment. Although maximizing the efficiency for regional gesture differences is still required, this approach could be a useful resource for public spaces such as service or educational institutions by lowering reliance on specialized hardware.