This research aims to develop a Sign Language Recognition (SLR) system capable of translating and interpreting sign language gestures into text in real time using deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This enhances communication between the speech and hearing-impaired population and the wider public, encouraging inclusivity, accessibility, and assistive technology adoption. If integrated with other systems, such a system can function as an automatic interpreter for the speech and hearing-impaired community and an SOS tool. They will save time and help users avoid frustration improving their ability to express themselves. The primary objective of this system is to enhance human–computer interaction (HCI) and natural language processing (NLP) capabilities in the assistive communication domain. By bridging the communication gap, this system allows individuals with hearing impairments or speech disorders to express themselves seamlessly, reducing misunderstandings and improving social integration. The research also explores gesture recognition, transfer learning, and real-time deployment to optimize the model for various applications, including healthcare, education, and emergency communication.