ABSTRACT We present a new X-Ray Accretion Disc-wind Emulator (xrade) based on the 2.5D Monte Carlo radiative transfer code that provides a physically motivated, self-consistent treatment of both absorption and emission from a disc wind by computing the local ionization state and velocity field within the flow. 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 disc-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 multidimensional spaces that are typically faced by traditional X-ray fitting packages such as xspec. 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 xrade to the physical interpretation of the X-ray spectra of the bright quasar PDS 456, which hosts the best-established accretion disc 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 microcalorimeters onboard future missions, like X-Ray Imaging and Spectroscopy Mission (XRISM)/Resolve and Athena/X-ray Integral Field Unit (X-IFU). This tool can also be implemented across a wide variety of X-ray spectral models and beyond.