  Dwarf galaxies are ideal laboratories to study the physics of the
interstellar medium (ISM). Emission lines have been widely used to this aim.
Retrieving the full information encoded in the spectra is therefore essential.
This can be efficiently and reliably done using Machine Learning (ML)
algorithms. Here, we apply the ML code GAME to MUSE (Multi Unit Spectroscopic
Explorer) and PMAS (Potsdam Multi Aperture Spectrophotometer) Integral Field
Unit (IFU) observations of two nearby Blue Compact Galaxies (BCGs): Henize 2-10
and IZw18. We derive spatially resolved maps of several key ISM physical
properties. We find that both galaxies show a remarkably uniform metallicity
distribution. Henize 2-10 is a star forming dominated galaxy, with a Star
Formation Rate (SFR) of about 1.2 M$_{\odot}$ yr$^{-1}$. Henize 2-10 features
dense and dusty ($A_V$ up to 5-7 mag) star forming central sites. We find IZw18
to be very metal-poor ($Z$= 1/20 $Z_{\odot}$). IZw18 has a strong interstellar
radiation field, with a large ionization parameter. We also use models of
PopIII stars spectral energy distribution as a possible ionizing source for the
HeII $\lambda$4686 emission detected in the IZw18 NW component. We find that
PopIII stars could provide a significant contribution to the line intensity.
The upper limit to the PopIII star formation is 52% of the total IZw18 SFR.
