ABSTRACT Very metal-poor stars ($\rm Fe/H \lt -2$) 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 ≈ 50) XP spectra, which provides an opportunity to greatly increase the number of candidate metal-poor stars. In this work, we utilize the XGBoost classification algorithm to identify ∼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. First, 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 per cent/45 per cent/76 per cent and completeness of 32 per cent/93 per cent/66 per cent, 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 per cent/52 per cent, respectively. We make our metal-poor star catalogues publicly available, for further exploration of the metal-poor Milky Way.