Physics‑Informed Neural Networks (PINNs) form the core of a new framework that merges IoT sensors with 3D asset models. The system layers analyze data and physics‑based simulations to predict degradation. Conservation teams can deploy the tool to monitor conditions in real time and schedule maintenance before damage occurs. The approach offers a scalable, data‑driven preservation method.