For the speech and hearing-impaired population, sign language is an essential means of communication. The accessibility of sign language, however, makes it extremely challenging to employ effectively with those who don’t comprehend it. This paper proposes to create a comprehensive system to translate sign language into both text and speech to overcome this communication gap. The system makes use of the convolutional neural network (CNN) technique to identify hand gestures made by people from images. The main purpose is to recognize hand motions from a camera image. The position of the hand and its orientation are then applied to obtain the training and testing data for the CNN. Firstly, the hand is detected from the image captured by the webcam then the image-processing Mediapipe library is used. After passing through a filter, this is sent to a classifier, which makes predictions about the class of hand movements. The CNN model is then trained using these pictures. This strategy could be advantageous to the hearing communities as well as the deaf/dumb people. Deaf/dumb individuals can express themselves more effectively than hearing individuals, while the latter can understand and respond to sign language gestures through text and speech. This paper explores the customization of sign language recognition for regional and cultural variations, making it adaptable to diverse sign languages worldwide.