  Identifying and characterizing young populations of star-forming regions is
crucial to unravel their properties. In this regard, Gaia-DR3 data and machine
learning tools are very useful for studying large star-forming complexes. In
this work, we analyze the $\rm \sim7.1degree^2$ area of one of our Galaxy's
dominant feedback-driven star-forming complexes, i.e., the region around
Trumpler 37. Using the Gaussian mixture and random forest classifier methods,
we identify 1243 high-probable members in the complex, of which $\sim60\%$ are
new members and are complete down to the mass limit of $\sim$0.1 $-$ 0.2~$\rm
M_{\odot}$. The spatial distribution of the stars reveals multiple clusters
towards the complex, where the central cluster around the massive star HD
206267 reveals two sub-clusters. Of the 1243 stars, 152 have radial velocity,
with a mean value of $\rm -16.41\pm0.72~km/s$. We investigate stars' internal
and relative movement within the central cluster. The kinematic analysis shows
that the cluster's expansion is relatively slow compared to the whole complex.
This slow expansion is possibly due to newly formed young stars within the
cluster. We discuss these results in the context of hierarchical collapse and
feedback-induced collapse mode of star formation in the complex.
