  We present machine learning (ML)-based pipelines designed to populate
galaxies into dark matter halos from N-body simulations. These pipelines
predict galaxy stellar mass ($M_*$), star formation rate (SFR), atomic and
molecular gas contents, and metallicities, and can be easily extended to other
galaxy properties and simulations. Our approach begins by categorizing galaxies
into central and satellite classifications, followed by their ML classification
into quenched (Q) and star-forming (SF) galaxies. We then develop regressors
specifically for the SF galaxies within both central and satellite subgroups.
We train the model on the $(100\mathrm{h^{-1}Mpc})^3$ Simba galaxy formation
simulation at $z=0$. Our pipeline yields robust predictions for stellar mass
and metallicity and offers significant improvements for SFR and gas properties
compared to previous works, achieving an unbiased scatter of less than 0.2 dex
around true Simba values for the halo-$M_{\rm HI}$ relation of central
galaxies. We also show the effectiveness of the ML-based pipelines at $z=1,2$.
Interestingly, we find that training on fraction-based properties (e.g. $M_{\rm
HI}$/$M_{*}$) and then multiplying by the ML-predicted $M_{*}$ yields improved
predictions versus directly training on the property value, for many quantities
across redshifts. However, we find that the ML-predicted scatter around the
mean is lower than the true scatter, leading to artificially suppressed
distribution functions at high values. To alleviate this, we add a "ML scatter
bias", finely tuned to recover the true distribution functions, critical for
accurate predictions of integrated quantities such as $\rm{HI}$ intensity maps.
