In the helicopter industry, estimating the flight loads acting on mechanical components is a prerequisite for fatigue computation, and maintenance work. Currently, the flight loads assessments are done during the development phase by the manufacturer, in line with the certification guidance material specifying that maintenance intervals be set – at fleet level - to an assumed usage that is “as severe as expected in service”. This paper aims at introducing a predictive maintenance approach, based on the real usage of each helicopter, where a reliable estimation of the flight loads could help to derive the damage of the helicopter components and therefore determine when to replace them. To this end, a study is conducted to link the quasi-static evolution of the flight parameters to the dynamic evolution of the flight loads. The proposed methodology is based on the locally periodic variations of the flight loads, by applying a harmonic decomposition over each period and by extracting their real and imaginary parts. With this decomposition, different types of Machine Learning models have been compared to predict each harmonic part. To evaluate the approach, the load is finally reconstructed using the predictions and compared to the original signal. The application on real data from proprietary databases shows the relevance of the approach. As locally periodic signals are represented in many domains, this approach could therefore be useful in other fields such as healthcare or the automotive industry, where signal estimation can be crucial.