  We developed and trained a pipeline of three machine learning (ML) models
than can predict which sources are more likely to be an AGN and to be detected
in specific radio surveys. Also, it can estimate redshift values for predicted
radio-detectable AGNs. These models, which combine predictions from tree-based
and gradient-boosting algorithms, have been trained with multi-wavelength data
from near-infrared-selected sources in the Hobby-Eberly Telescope Dark Energy
Experiment (HETDEX) Spring field. Training, testing, calibration, and
validation were carried out in the HETDEX field. Further validation was
performed on near-infrared-selected sources in the Stripe 82 field. In the
HETDEX validation subset, our pipeline recovers 96% of the initially labelled
AGNs and, from AGNs candidates, we recover 50% of previously detected radio
sources. For Stripe 82, these numbers are 94% and 55%. Compared to random
selection, these rates are two and four times better for HETDEX, and 1.2 and 12
times better for Stripe 82. The pipeline can also recover the redshift
distribution of these sources with $\sigma_{\mathrm{NMAD}}$ = 0.07 for HETDEX
($\sigma_{\mathrm{NMAD}}$ = 0.09 for Stripe 82) and an outlier fraction of 19%
(25% for Stripe 82), compatible with previous results based on broad-band
photometry. Feature importance analysis stresses the relevance of near- and
mid-infrared colours to select AGNs and identify their radio and redshift
nature. Combining different algorithms in ML models shows an improvement in the
prediction power of our pipeline over a random selection of sources. Tree-based
ML models (in contrast to deep learning techniques) facilitate the analysis of
the impact that features have on the predictions. This prediction can give
insight into the potential physical interplay between the properties of radio
AGNs (e.g. mass of black hole and accretion rate).
