Structural Health Monitoring (SHM) plays a crucial role in assessing the integrity of historic buildings. This work focuses on the development of machine learning models to analyze the dynamic response of the San Cristobal Church in Cusco, Peru, a 17th century World Heritage Site instrumented with a seismic sensor. The study is carried out within the framework of the IRD-funded ARCHIVES project. The main objective of this research is to model the resonant frequency of the church using environmental parameters such as temperature, humidity, and atmospheric pressure. Three key tasks are explored: (1) developing a predictive model of resonant frequency based on weather conditions, (2) using explanatory techniques to assess the impact of individual weather variables, and (3) performing anomaly detection to identify unusual structural responses, particularly after earthquakes.