Sign language is a vital means of communication for the deaf and hard-of-hearing community. However, translating Indian Sign Language (ISL) into regional languages like Odia remains a significant technological challenge due to the language’s rich morphology, agglutinative grammar, and complex script. This work presents a real-time ISL recognition and translation system that converts hand gestures into Odia text, enhancing accessibility and promoting inclusive communication. The system leverages MediaPipe for real-time key-point detection and uses a custom-built dataset of 1,200 samples across 12 ISL gesture classes, captured under diverse Indian backgrounds and lighting conditions to ensure robustness. Both 2D and 3D Convolutional Neural Networks (CNNs) were explored, with the 2D CNN achieving superior performance 98.33% test accuracy compared to the 3D CNN’s 78.33%. Recognized gestures are translated into Odia using a curated gesture-to-text mapping dictionary, seamlessly integrated into a lightweight Tkinter-based GUI. Unlike other resource-heavy systems, this model is optimized for deployment on low-resource devices, making it suitable for rural and educational contexts. Beyond translation, the system can function as an assistive learning tool for students and educators of ISL. This work demonstrates that combining culturally curated datasets with efficient AI models can bridge communication gaps and create regionally adapted, accessible technology for the deaf and mute community in India.