Visual-based translator systems, which utilize image and video data for real-time translation, represent a captivating research area. While previous research has explored text-based translation, limitations exist in capturing the nuances of human communication. Visual-based approaches address this gap by leveraging deep learning and image processing to extract information like objects, faces, and environmental context. This research project investigates the application of deep learning in sign language translation using Automated AI. By analyzing visual data, the system recognizes signs and translates them into spoken languages or generates text descriptions. This technology holds significant promise for various fields - education, entertainment, tourism, and healthcare - and can contribute to the advancement of information technology and artificial intelligence systems.