Civil engineering structures, such as bridges and viaducts, are subjected to environmental stresses, material fatigue, and excessive loads, all contributing to gradual degradation. This deterioration poses significant risks to both safety and functionality. Structural Health Monitoring (SHM) has become essential for ensuring the integrity and longevity of these infrastructures through continuous assessment. By leveraging advanced sensors and data-driven techniques, SHM enables early anomaly detection, facilitating timely interventions and optimized maintenance strategies. This thesis explores the integration of Machine Learning (ML) and Deep Learning (DL) to further enhance SHM by improving the processing of large-scale data and boosting the accuracy of anomaly detection.