  In this paper, we carry out an assessment of cosmic distance duality relation
(CDDR) based on the latest observations of HII galaxies acting as standard
candles and ultra-compact structure in radio quasars acting as standard rulers.
Particularly, two machine learning reconstruction methods (Gaussian Process
(GP) and Artificial Neural Network (ANN)) are applied to reconstruct the Hubble
diagrams from observational data. We show that both approaches are capable of
reconstructing the current constraints on possible deviations from the CDDR in
the redshift range $z\sim 2.3$. Considering four different parametric methods
of CDDR, which quantify deviations from the CDDR and the standard cosmological
model, we compare the results of the two different machine learning approaches.
It is observed that the validity of CDDR is in well agreement with the current
observational data within $1\sigma$ based on the reconstructed distances
through GP in the overlapping redshift domain. Moreover, we find that
ultra-compact radio quasars could provide $10^{-3}$-level constraints on the
violation parameter at high redshifts, when combined with the observations of
HII galaxies. In the framework of ANN, one could derive robust constraints on
the violation parameter at a precision of $10^{-2}$, with the validity of such
distance duality relation within $2\sigma$ confidence level.
