  We present a new X-Ray Accretion Disk-wind Emulator (\textsc{xrade}) based on
the 2.5D Monte Carlo radiative transfer code which provides a
physically-motivated, self-consistent treatment of both absorption and emission
from a disk-wind by computing the local ionization state and velocity field
within the flow. \textsc{xrade} is then implemented through a process that
combines X-ray tracing with supervised machine learning. We develop a novel
emulation method consisting in training, validating, and testing the simulated
disk-wind spectra into a purposely built artificial neural network. The trained
emulator can generate a single synthetic spectrum for a particular parameter
set in a fraction of a second, in contrast to the few hours required by a
standard Monte Carlo radiative transfer pipeline. The emulator does not suffer
from interpolation issues with multi-dimensional spaces that are typically
faced by traditional X-ray fitting packages such as \textsc{xspec}.
\textsc{xrade} will be suitable to a wide number of sources across the
black-hole mass, ionizing luminosity, and accretion rate scales. As an example,
we demonstrate the applicability of \textsc{xrade} to the physical
interpretation of the X-ray spectra of the bright quasar PDS 456, which hosts
the best-established accretion-disk wind observed to date. We anticipate that
our emulation method will be an indispensable tool for the development of
high-resolution theoretical models, with the necessary flexibility to be
optimized for the next generation micro-calorimeters on board future missions,
like \textit{XRISM/resolve} and \textit{Athena/X-IFU}. This tool can also be
implemented across a wide variety of X-ray spectral models and beyond.
