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Accurate photo-z measurements are important to construct a large-scalestructure map of X-ray Universe in the ongoing SRG/eROSITA All-Sky Survey. Wepresent machine learning Random Forest-based models for probabilistic photo-zpredictions based on information from 4 large photometric surveys (SDSS,Pan-STARRS, DESI Legacy Imaging Survey, and WISE). Our models are trained onthe large sample of $\approx$580000 quasars and galaxies selected from the SDSSDR14 spectral catalog and take into account Galactic extinction anduncertainties in photometric measurements for target objects. On the Stripe82Xtest sample we obtained photo-z accuracy for X-ray sources: $NMAD=0.034$(normalized median absolute deviation) and $n_{>0.15}=0.088$ (catastrophicoutliers fraction), which is almost $\sim2$ times better than best photo-zresults available in the literature.

Probabilistic photo-z machine learning models for X-ray sky surveys