The vulnerability of historic masonry structures to seismic events represents a significant challenge for the preservation of Europe’s cultural heritage. Structural Health Monitoring (SHM) techniques offer a promising solution for detecting degradation and damage progression, enabling timely and targeted retrofit interventions. However, the limited availability of real-world labeled data related to damage conditions hinders the development of reliable predictive models for structural anomaly detection. To address this limitation, this work proposes a methodology based on finite element micromechanical modelling of an archetypal masonry panel and a realistic masonry frame, consisting of a typical facade, both subjected to in-plane loading to simulate seismic-induced damage. Specifically, damage is simulated at various levels of severity, representing different stages of structural degradation. Monitoring data are generated numerically by simulating the use of smart bricks, a new type of strain sensor for SHM of masonry structures. The information obtained from the archetypal panel is used to train a Domain-Adversarial Neural Network, enabling knowledge transfer from the archetypal panel to the facade model. This domain adaptation strategy effectively overcomes data scarcity and enhances the generalization capability of the damage classifier. The obtained results demonstrate the effectiveness of the method in detecting structural anomalies. Overall, the proposed approach shows strong potential for real-world SHM applications in historic masonry structures, supporting continuous monitoring.