  Very metal-poor stars ($\rm[Fe/H] < -2$) in the Milky Way are fossil records
of early chemical evolution and the assembly and structure of the Galaxy.
However, they are rare and hard to find. Gaia DR3 has provided over 200 million
low-resolution ($R \approx 50$) XP spectra, which provides an opportunity to
greatly increase the number of candidate metal-poor stars. In this work, we
utilise the \texttt{XGBoost} classification algorithm to identify $\sim$200,000
very metal-poor star candidates. Compared to past work, we increase the
candidate metal-poor sample by about an order of magnitude, with comparable or
better purity than past studies. Firstly, we develop three classifiers for
bright stars ($BP$ $<$ 16). They are Classifier-T (for Turn-off stars),
Classifier-GC (for Giant stars with high completeness), and Classifier-GP (for
Giant stars with high purity) with expected purity of 52\%/45\%/76\% and
completeness of 32\%/93\%/66\% respectively. These three classifiers obtained a
total of 11,000/111,000/44,000 bright metal-poor candidates. We apply model-T
and model-GP on faint stars ($BP$ $>$ 16) and obtain 38,000/41,000 additional
metal-poor candidates with purity 29\%/52\%, respectively. We make our
metal-poor star catalogs publicly available, for further exploration of the
metal-poor Milky Way.
