The diversity of sign languages, as complex visual and gestural systems, has been shaped by cultural and regional factors. This paper presents a new framework that compares Moroccan Sign Language (MSL) with international sign languages, such as American or Japanese Sign Language (A-JSL). Based on video processing techniques and human estimation models, we extract key points from gestures and analyze similarities and differences using dimensionality reduction, clustering and similarity measures. Through visualizing these relationships in a reduced feature space, we uncover shared and distinct patterns in gestures, leading to a better understanding of the linguistic universals and cultural specificities of sign languages. These results highlight the potential for standardizing gestures across languages, and support the development of interoperable tools for sign language translation and recognition. This work provides the basis for further studies on the computational analysis of sign language linguistics.