This proposal presents an innovative framework for cultural heritage documentation aiming to overcome the multifaceted threats that face cultural heritage sites. The complex interdependencies between heritage sites (e.g., archaeological, religious, and cultural) are modeled in the form of a network model. This model includes nodes (sites) and edges (shared history or geography). Then, Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) are used to simulate degradation, prioritize conservation efforts, and generate high-fidelity 3D reconstructions from fragmented data. Preliminary results show that this networked approach identifies critical "keystone" sites whose preservation is essential for maintaining the integrity of the entire heritage ecosystem. The accuracy of GNN is 87% in predicting secondary damage patterns. The GAN successfully generates 3D reconstructions (20% of original scan data).