Snow on Arctic sea ice significantly influences regional and global climate processes, yet accurately modelling its spatial and temporal variability remains challenging. We introduce a physics-guided Long Short-Term Memory (LSTM) emulator developed to efficiently simulate pan-Arctic snow density and depth distributions. The emulator is trained on high-resolution SnowModel-3D simulations that resolve wind-driven snow redistribution over sea-ice surface topography, and is applied over the Lagrangian parcel framework of SnowModel-LG for pan-Arctic inference. The topographic input required for inference comes from CryoSat-2 surface roughness estimates at a pan-Arctic scale. We assessed the emulator’s predictive accuracy across multiple years (2014–2021) and various seasonal conditions. Compared with airborne snow observation campaigns, the emulated snow depth has an RMSE of 0.08–0.10 m. By representing the influence of sea-ice topography on snow redistribution, which is not accounted for in SnowModel-LG, the emulator adds a process that brings the simulated snow depth closer to these airborne observations. While seasonal and interannual trends in snow density and depth were effectively captured, emulator accuracy varied during complex melt periods. Our emulator provides an efficient tool for enhancing pan-Arctic snow modelling capabilities in climate studies.