This study investigates the sensitivity of the radio interferometers to identify imprints of spatially inhomogeneous dark matter annihilation signatures in the 21-cm signal during the prereionization era. We focus on the upcoming low-mode survey of the Square Kilometre Array (SKA-Low) telescope. Using convolutional neural networks (CNNs), we analyze simulated 3D 21-cm differential brightness temperature maps generated via the dm21cm code, which is based on 21cmfast and darkhistory, to distinguish between spatially homogeneous and inhomogeneous energy injection/deposition scenarios arising from dark matter annihilation. The inhomogeneous case accounts for local dark matter density contrasts and gas properties, such as thermal and ionization states, while the homogeneous model assumes uniform energy deposition. Our study focuses on two primary annihilation channels to electron-positron pairs $(e^{+}{e}^{-})$ and photons $(\gamma \gamma )$, exploring dark matter masses from 1 MeV to 100 MeV and a range of annihilation cross sections. For the $\gamma \gamma$ channel, the distinction across dark matter models is less pronounced due to the larger mean free path of the emitted photons, resulting in a more uniform energy deposition. For $e^{+}{e}^{-}$ channel, the results indicate that the CNNs can effectively differentiate between the inhomogeneous and homogeneous cases. Despite observational challenges, the results demonstrate that these effects remain detectable even after incorporating noise from next-generation radio interferometers, such as the SKA. We find that the inhomogeneous dark matter annihilation models can leave measurable imprints on the 21-cm signal maps distinguishable from the homogeneous scenarios for the dark matter masses $m_{\mathrm{DM}}=1\mathrm{MeV}$ and the annihilation cross sections of $⟨\sigma v⟩\ge 5\times {10}^{-30}{\mathrm{cm}}^3/\mathrm{s}$ ($⟨\sigma v⟩\ge 5\times {10}^{-29}{\mathrm{cm}}^3/\mathrm{s}$ for $m_{\mathrm{DM}}=100\mathrm{MeV}$) for moderate SKA-Low noise. Our results demonstrate the potential of machine learning techniques, such as CNNs, to extract subtle features from complex astrophysical data and sensitivity to probe exotic sources of energy injections, offering a novel approach to exploring the nature of dark matter and the early Universe with future 21-cm observations.

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• Dark matter
• Particle dark matter
• Astrophysical & cosmological simulations