Earthquake is one of the natural phenomena that threatens human life in areas around faults. Mathematical transformations are tools that can provide useful information to researchers in generating synthetic accelerograms, analyzing signals and accelerograms. With the help of mathematical transformations, recorded accelerograms of earthquakes can be considered as input signals and analyzed. Using Fourier transform, the frequencies present in the signal can be obtained, but it does not provide the temporal (or spatial) information of the signal’s constituent frequencies. The wavelet transform is a tool suitable for analyzing non-stationary waves and compensates for this weakness by providing not only the frequencies present in the signal but also the time (or location) of the frequencies. By changing the scale and translation parameters in the wavelet transform, the constituent frequencies of the earthquake wave and their occurrence time can be obtained. In this research, frequency analysis using wavelet transform was performed on the accelerograms of the 2020 Masha earthquake in near, intermediate, and far fields. Based on this analysis, the dominant frequencies of each wave and their occurrence moments were obtained, showing that in the near field, the dominant frequency of the earthquake wave is larger compared to the distant field. In other words, as the distance from the fault increases, the dominant frequency of the earthquake wave decreases, which is dangerous for tall structures with high natural period due to the resonance phenomenon. The results indicate the high capability of the continuous wavelet transform method in calculating the constituent frequencies of earthquake waves and their occurrence times. With this analysis, structures whose period matches the dominant period of the earthquake wave can be identified, and the effects of waves on structures can be examined more accurately.