  The galaxy total mass inside the effective radius encode important
information on the dark matter and galaxy evolution model. Total "central"
masses can be inferred via galaxy dynamics or with gravitational lensing, but
these methods have limitations. We propose a novel approach, based on Random
Forest, to make predictions on the total and dark matter content of galaxies
using simple observables from imaging and spectroscopic surveys. We use
catalogs of multi-band photometry, sizes, stellar mass, kinematic
"measurements" (features) and dark matter (targets) of simulated galaxies, from
Illustris-TNG100 hydrodynamical simulation, to train a Mass Estimate machine
Learning Algorithm (Mela). We separate the simulated sample in passive
early-type galaxies (ETGs), both "normal" and "dwarf", and active late-type
galaxies (LTGs) and show that the mass estimator can accurately predict the
galaxy dark masses inside the effective radius in all samples. We finally test
the mass estimator against the central mass estimates of a series of low
redshift (z$\leq$0.1) datasets, including SPIDER, MaNGA/DynPop and SAMI dwarf
galaxies, derived with standard dynamical methods based on Jeans equations.
Dynamical masses are reproduced within 0.30 dex ($\sim2\sigma$), with a limited
fraction of outliers and almost no bias. This is independent of the
sophistication of the kinematical data collected (fiber vs. 3D spectroscopy)
and the dynamical analysis adopted (radial vs. axisymmetric Jeans equations,
virial theorem). This makes Mela a powerful alternative to predict the mass of
galaxies of massive stage-IV surveys' datasets.
