This thesis presents the research and development of Machine Learning and AI techniques for the analysis and processing of cultural heritage data. The main objective is to design a point cloud segmentation application for cultural monuments, addressing the challenges posed by large and heterogeneous data. Two approaches are explored: a machine learning classifier trained on a partially segmented point cloud to label the remaining points, and neural network models trained on annotated datasets to segment points into predefined classes. Despite limitations caused by the scarcity of labeled data, both methods proved effective and are integrated into a user-friendly interface, enabling users to load point clouds, select an approach, adjust parameters, and execute the segmentation.