Real-time sign language is a basic means of communication for hearing-impaired people. There is a substantial communication barrier between sign language users and those who cannot comprehend sign language. Indian Sign Language (ISL) is built to ease the social challenge between hearing-impaired people and individuals who are unable to understand sign language using TensorFlow featuring Indian languages for smoother communication. The study aims to build a proficient real-time sign language translator using TensorFlow to detect hand signals in real-time video streams. Integration of TensorFlow enables real-time gesture detection, demonstrating how technology can bring about real progress when it comes to improving communication with persons who cannot hear. The application is trained on a specialized dataset comprising different Indian sign language signals, pre-processed to improve gesture recognition focusing on fast and accurate sign language recognition and translation. The objective is to develop a model that can recognize and translate hand gestures into text in Indian languages. This approach uses TensorFlow object detection API to recognize body gestures from real-time videos. The model is trained on a unique dataset of diverse Indian languages that is pre-processed for better recognition accuracy. Techniques like transfer learning are employed to fine-tune the model by integrating CNN for gesture recognition. The detected outputs are afterward transformed into Indian languages. The system’s accuracy may be restricted because of the quality and variety of different Indian languages across the country. The findings indicate that the model can accurately translate the collection of sign languages into text highlighting the potential of TensorFlow Object Detection for real-time sign language.