A comprehensive analysis of the spectral features of acoustic emissions in rock samples has been undertaken, employing advanced machine learning algorithms. Findings suggest that these data analysis methods enable the automated identification of events, enhance the precision in frequency estimation, and facilitate the prediction of potential seismic activity with greater accuracy.