The Neutrinos Angra Experiment aims at developing a system capable of monitoring the fuel-burning process of nuclear reactors. This system is based on an antineutrino detector located a few meters far from the core of the Angra II nuclear reactor. One of the main parameters used to select antineutrino events is the released energy left by a particle when interacting with the detector medium which, in turn, is proportional to the amplitude and area of the signals generated by the detector's transducers. However, the estimation of such quantities may lose accuracy due to saturation effects caused by the experiment electronics, making it necessary to apply methods to compensate or correct such effects. This work proposes to implement and test machine learning algorithms designed to estimate the energy of saturated signals.