  Cosmic-ray antimatter, particularly low-energy antideuterons, serves as a
sensitive probe of dark matter annihilating in our Galaxy. We study this
smoking-gun signature and explore its complementarity with indirect dark matter
searches using cosmic-ray antiprotons. To this end, we develop the neural
network emulator $\overline{\text{D}}$arkRayNet, enabling a fast prediction of
propagated antideuteron energy spectra for a wide range of annihilation
channels and their combinations. We revisit the Monte Carlo simulation of
antideuteron coalescence and cosmic-ray propagation, allowing us to explore the
uncertainties of both processes. In particular, we take into account
uncertainties from the $\Lambda_b$ production rate and consider two distinctly
different propagation models. Requiring consistency with cosmic-ray antiproton
limits, we find that AMS-02 shows sensitivity to a few windows of dark matter
masses only, most prominently below 20 GeV. This region can be probed
independently by the upcoming GAPS experiment. The program package
$\overline{\text{D}}$arkRayNet is available on GitHub.
