The Real-Time Indian Sign Language (ISL) Translator project aims to bridge the communication gap between the deaf and hard-of-hearing community and the hearing world by leveraging advanced deep learning techniques. This project utilizes Convolutional Neural Networks (CNNs) to recognize the ISL gesture and converts them into text. The recognized text is then translated into speech in multiple Indian languages, allowing seamless real-time communication. The system is comprised of three primary components: the Gesture Recognition Module, which employs a CNN to process real-time video input of ISL gestures; the Text processing Module, which converts the recognized gesture into structured text; and the Text-to-Speech (TTS) Module, which translates the test into speech in various Indian languages. An additional Language Selection Module provides users with the option to choose their preferred language for the TTS output. To ensure accessibility and usability, this project integrates a user-friendly website interface, enabling individuals to use the translator easily across different platform. By fostering greater inclusion and improving communication in social, educational, and professional settings, the Real-Time ISL Translator project stands as a significant technological advancement for the deaf and hard-of-hearing community in India.