  Gamma-Ray Bursts (GRBs), due to their high luminosities are detected up to
redshift 10, and thus have the potential to be vital cosmological probes of
early processes in the universe. Fulfilling this potential requires a large
sample of GRBs with known redshifts, but due to observational limitations, only
11\% have known redshifts ($z$). There have been numerous attempts to estimate
redshifts via correlation studies, most of which have led to inaccurate
predictions. To overcome this, we estimated GRB redshift via an ensemble
supervised machine learning model that uses X-ray afterglows of long-duration
GRBs observed by the Neil Gehrels Swift Observatory. The estimated redshifts
are strongly correlated (a Pearson coefficient of 0.93) and have a root mean
square error, namely the square root of the average squared error
$\langle\Delta z^2\rangle$, of 0.46 with the observed redshifts showing the
reliability of this method. The addition of GRB afterglow parameters improves
the predictions considerably by 63\% compared to previous results in
peer-reviewed literature. Finally, we use our machine learning model to infer
the redshifts of 154 GRBs, which increase the known redshifts of long GRBs with
plateaus by 94\%, a significant milestone for enhancing GRB population studies
that require large samples with redshift.
