Communication barriers persist for individuals who use sign language as their primary mode of interaction, particularly in digital environments where conversational AI bots are increasingly prevalent. This paper presents a novel approach to addressing these barriers through the development of a Sign Language Recognition System (SLRS) integrated with conversational AI capabilities. The SLRS utilizes advanced computer vision techniques and machine learning algorithms, specifically employing a Random Forest Classifier, achieving an exceptional accuracy of 0.9961. Additionally, a comparison between CNN, KNN, and Random Forest classifiers underscores the superiority of Random Forest in accuracy and performance. Integration with the conversational AI bot “Gopal,” powered by the Google Gemini Model, facilitates natural language interactions, allowing users who use sign language to communicate seamlessly with digital systems. Evaluation of the SLRS demonstrates promising performance in accurately recognizing a diverse range of sign language gestures and generating contextually relevant responses. User testing sessions highlight the system’s responsiveness and effectiveness in bridging the communication gap between users who use sign language and conversational AI bots. The development of the SLRS represents a significant step towards promoting inclusivity and accessibility in digital communication for those who communicate with sign language. By enabling seamless interaction with conversational AI bots, the SLRS empowers users who use sign language to engage more effectively with digital interfaces and access information effortlessly. Continued research and refinement of the SLRS hold the potential to further enhance communication accessibility and foster greater inclusion in digital environments.