  We have computed obscured AGN redshifts using the XZ method, adopting a broad
treatment in which we employed a wide-ranging data set and worked primarily at
the XZ counts sensitivity threshold, culminating with a redshift catalog
containing 121 sources that lack documented redshifts. We considered 363
obscured AGN from the Chandra Source Catalog Release 2.0, 59 of which were
selected using multiwavelength criteria while 304 were X-ray selected.
One-third of the data set had cross-matched spectroscopic or photometric
redshifts. These sources, dominated by low-$z$ and low-$N_H$ AGN, were
supplemented by 1000 simulations to form a data set for testing the XZ method.
We used a multi-layer perceptron neural network to examine and predict cases in
which XZ fails to reproduce the known redshift, yielding a classifier that can
identify and discard poor redshift estimates. This classifier demonstrated a
statistically significant $\sim$3$\sigma$ improvement over the existing XZ
redshift information gain filter. We applied the machine learning model to
sources with no documented redshifts, resulting in the 121-source new redshift
catalog, all of which were X-ray selected. Our neural network's performance
suggests that nearly 90% of these redshift estimates are consistent with
hypothetical spectroscopic or photometric measurements, strengthening the
notion that redshifts can be reliably estimated using only X-rays, which is
valuable to current and future missions such as Athena. We have also identified
a possible Compton-thick candidate that warrants further investigation.
