This project presents a Nepali Sign Language Recognition system designed to bridge the communication gap between the Deaf community and hearing individuals. The system uses a 2D Convolutional Neural Network (CNN) to automatically recognize hand gestures representing Nepali alphabets and numbers. Image preprocessing techniques such as grayscale conversion, thresholding, edge detection, and contour detection are applied to extract meaningful hand gesture shapes before feeding them into the neural network. The CNN architecture includes convolutional, pooling, nonlinear, and fully connected layers, with ReLU activation used to introduce non-linearity. The model is trained on a dataset of 56,400 images covering 37 alphabets and 10 numerals, with data augmentation techniques such as horizontal flipping and inclusion of blank images to improve robustness. These enhancements significantly improved model performance, increasing accuracy from 82.45% to 92.46%. The system demonstrates the effectiveness of deep learning in sign language recognition and promotes accessible communication technology.