Morocco is a diverse and rich melting pot of cultures, ranging from pottery, handcrafted copper cookware, to the highly recognized and unique traditional Mosaic tiles known as Zellij, kaftans, and calligraphy. These cultural trademarks each possess distinct characteristics that have been crafted through centuries-old traditions. However, as these cultural artifacts spread worldwide and began being incorporatedss into many households and architectural marvels across neighboring countries, their pure Moroccan heritage has been increasingly absorbed by cultural appropriation. This necessitates a stand to defend and preserve the cultural importance of these artifacts and their indispensable place in Moroccan culture. Our goal with this study is to develop methods for distinguishing authentic Moroccan cultural trademarks from imitations, using shape detection and analysis. For now, we focus on Zellij tiles, utilizing Artificial Intelligence technologies to count and analyze shapes within the tiles. Specifically, we implement edge-based segmentation models to perform shape detection, calculating parameters such as the axes of each contour, surface area, and symmetry to identify and count the shapes. The AI technologies used involve specific models trained on a dataset of Zellij tiles obtained from various sources. In this study, we aim to showcase the effectiveness of these models in accurately classifying Zellij tiles and the impact of different model alterations on performance. These models are integrated into a mobile application, facilitating real-time interaction for users to determine the origin of a tile or mosaic with a degree of confidence in the classification results (e.g., Moroccan or foreign). Our broader aim is to extend this approach to classify other Moroccan cultural trademarks such as kaftans and calligraphy, thereby preserving and promoting the rich cultural heritage of Morocco.