  We develop a random forest regressor (RFR) machine learning model to trace
the coronal evolution in two highly variable active galactic nuclei (AGNs) IRAS
13224-3809 and 1H 0707-495 observed with XMM-Newton, by probing the X-ray
reverberation features imprinted on their power spectral density (PSD)
profiles. Simulated PSDs in the form of a power-law, with similar frequency
range and bins to the observed data, are produced. Then, they are convolved
with relativistic disc-response functions from a lamp-post source before being
used to train and test the model to predict the coronal height. We remove some
bins that are dominated by Poisson noise and find that the model can tolerate
the frequency-bin removal up to $\sim 10$ bins to maintain a prediction
accuracy of $R^{2} > 0.9$. The black hole mass and inclination should be fixed
so that the accuracy in predicting the source height is still $> 0.9$. The
accuracy also increases with the reflection fraction. The corona heights for
both AGN are then predicted using the RFR model developed from the simulated
PSDs whose frequency range and bins are specifically adjusted to match those
from each individual observation. The model suggests that their corona varies
between $\sim~5 - 18~r_{\rm g}$, with $R^{2} > 0.9$ for all observations. Such
high accuracy can still be obtained if the difference between the true mass and
the trained value is $\lesssim 10\%$. Finally, the model supports the
height-changing corona under the light-bending scenario where the height is
correlated to source luminosity in both IRAS 13224-3809 and 1H 0707-495.
