  With the advent of deep, all-sky radio surveys, the need for ancillary data
to make the most of the new, high-quality radio data from surveys like the
Evolutionary Map of the Universe (EMU), GLEAM-X, VLASS and LoTSS is growing
rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei
(AGNs), and have a significantly higher average redshift when compared with
optical and infrared all-sky surveys. Thus, traditional methods of estimating
redshift are challenged, with spectroscopic surveys not reaching the redshift
depth of radio surveys, and AGNs making it difficult for template fitting
methods to accurately model the source. Machine Learning (ML) methods have been
used, but efforts have typically been directed towards optically selected
samples, or samples at significantly lower redshift than expected from upcoming
radio surveys. This work compiles and homogenises a radio-selected dataset from
both the northern hemisphere (making use of SDSS optical photometry), and
southern hemisphere (making use of Dark Energy Survey optical photometry). We
then test commonly used ML algorithms such as k-Nearest Neighbours (kNN),
Random Forest, ANNz and GPz on this monolithic radio-selected sample. We show
that kNN has the lowest percentage of catastrophic outliers, providing the best
match for the majority of science cases in the EMU survey. We note that the
wider redshift range of the combined dataset used allows for estimation of
sources up to z = 3 before random scatter begins to dominate. When binning the
data into redshift bins and treating the problem as a classification problem,
we are able to correctly identify $\approx$76% of the highest redshift sources
- sources at redshift z $>$ 2.51 - as being in either the highest bin (z $>$
2.51), or second highest (z = 2.25).
