  Active Galactic Nuclei (AGN) are relevant sources of radiation that might
have helped reionising the Universe during its early epochs. The super-massive
black holes (SMBHs) they host helped accreting material and emitting large
amounts of energy into the medium. Recent studies have shown that, for epochs
earlier than $z~{\sim}~5$, the number density of SMBHs is on the order of few
hundreds per square degree. Latest observations place this value below $300$
SMBHs at $z~{\gtrsim}~6$ for the full sky. To overcome this gap, it is
necessary to detect large numbers of sources at the earliest epochs. Given the
large areas needed to detect such quantities, using traditional redshift
determination techniques -- spectroscopic and photometric redshift -- is no
longer an efficient task. Machine Learning (ML) might help obtaining precise
redshift for large samples in a fraction of the time used by other methods. We
have developed and implemented an ML model which can predict redshift values
for WISE-detected AGN in the HETDEX Spring Field. We obtained a median
prediction error of $\sigma_{z}^{N} = 1.48 \times (z_{\mathrm{Predicted}} -
z_{{\mathrm{True}}}) / (1 + z_{\mathrm{True}}) = 0.1162$ and an outlier
fraction of $\eta = 11.58 \%$ at $(z_{\mathrm{Predicted}} -
z_{{\mathrm{True}}}) / (1 + z_{\mathrm{True}}) > 0.15$, in line with previous
applications of ML to AGN. We also applied the model to data from the Stripe 82
area obtaining a prediction error of $\sigma_{z}^{N} = 0.2501$.
