Sign language is an essential communication method for individuals with hearing and speech disabilities, facilitating their self-expression and interaction with the wider community. This project aims to create a reliable and precise system for sign language recognition utilizing advanced deep learning methodologies. The proposed model attains an accuracy of 98% in classifying diverse sign gestures through the application of Convolutional Neural Networks (CNN) and Transfer Learning (TL). Transfer learning utilizes pre-trained models to improve feature extraction and refine the classification process, particularly in scenarios with limited training data. This methodology ensures superior performance with varied applications in the field of education, healthcare, and everyday communication. Current methodologies, such as Basic CNN and Recurrent Neural Networks (RNN), attain an accuracy of 85%, highlighting the efficacy of the proposed algorithms. The amalgamation of CNN and TL enhances recognition precision while concurrently diminishing computational demands. This system possesses significant potential for real-time applications, closing communication gaps and promoting inclusivity for individual’s dependent on sign language. The project can significantly advance assistive technologies and promotes accessibility by integrating cutting-edge technology with user-centered design.