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The paper introduces an IoT-enabled system for real-time sign language recognition and voice conversion to improve communication for people with hearing or speech impairments. Using deep learning with TensorFlow, the model accurately detects hand gestures from American and Chinese Sign Language through a standard webcam, with OpenCV handling image processing and pyttsx3 converting recognized signs into speech. An ESP32 microcontroller transmits the interpreted data over Wi-Fi and hosts a mobile-friendly web page, eliminating the need for extra hardware or dedicated apps. This low-cost, efficient solution achieves high real-time accuracy, offering both audio and visual feedback, and showcases the effective integration of AI and IoT in bridging communication gaps.