Sign language recognition is pivotal in enhancing communication and accessibility for individuals with hearing impairments. This paper introduces an innovative system leveraging Long Short-Term Memory (LSTM) networks, a form of recurrent neural network renowned for capturing temporal dependencies in sequential data. The objective is to proficiently interpret and classify sign language gestures extracted from real-time video or image data. The proposed system employs a comprehensive methodology encompassing dataset selection, preprocessing, feature extraction, LSTM model architecture design, training, and evaluation. A diverse, well-annotated dataset is utilized, covering various sign language gestures with corresponding labels. The LSTM model is meticulously configured with appropriate architecture parameters, including the number of LSTM layers and memory cells. Experimental results underscore the robustness and efficiency of the proposed sign language recognition system. Real-time capabilities are achieved by integrating the trained LSTM model into a user-friendly interface for live video or image data processing. The system undergoes performance and usability evaluation through user studies, providing insights for future enhancements and applications. This LSTM-based sign language recognition system exhibits promising potential in enhancing accessibility and communication for individuals with hearing and speech impairments. Deep learning techniques and temporal modeling contribute to accurate interpretation and classification of sign language gestures, fostering improved inclusion and interaction within the deaf community.