  A large fraction of Fermi-LAT sources in the 4th Fermi LAT 14-year Catalog
(4FGL) still remain unidentified (unIDed). We continued to improve our machine
learning pipeline (MUWCLASS) and used it to classify 1206 X-ray sources located
within the extent of 73 unIDed 4FGL sources with Chandra X-ray Observatory
(CXO) observations included in the Chandra Source Catalog 2.0. Recent
improvements to our pipeline include astrometric corrections, probabilistic
cross-matching to lower-frequency counterparts, and a more realistic
oversampling method. X-ray sources are classified into 8 broad pre-determined
astrophysical classes defined in the updated training dataset which we also
release. The classifications give 115 plausible X-ray counterparts to 45 GeV
sources. We present details of the machine learning classification, describe
the pipeline improvements, and perform additional spectral and variability
analysis for brighter sources. We identify 3 GeV sources as isolated neutron
star candidates, 1 as an X-ray binary candidate, 16 as active galactic nucleus
candidates, 7 sources are associated with star-forming regions, and 9 are
ambiguous cases. For the remaining 37 unIDed 4FGL sources, we could not
identify any plausible counterpart in X-rays or they are too close to the
Galactic Center. Finally, we outline the observational strategies and further
improvements in the pipeline that can lead to more accurate classifications.
