This study presents a statistical analysis of the microphysical properties and ice crystal habits of low-level Arctic mixed-phase clouds (MPCs) using in situ observations from four aircraft campaigns conducted near Svalbard, Norway. The dataset comprises 45 hours of in-cloud measurements, with 731 600 ice crystal images classified using a convolutional neural network. Observations were categorized into four meteorological regimes using the Marine Cold Air Outbreak index: Marine Cold Air Outbreaks (MCAOs), Warm Air Advections (WAAs), and two transitional states. Vertical profiles of microphysical properties exhibit clear differences between regimes. MCAO conditions are associated with deeper mixed-phase layers, larger ice water contents, and higher concentrations of ice particles. In contrast, WAA cases are associated with shallower clouds dominated by liquid water with lower ice water content. The analysis indicates that temperature inversions influence cloud microphysics: weaker inversions support deeper clouds and enhanced ice development, while stronger inversions limit the extent of the liquid-containing layer. Analysis of ice crystal habits reveals distinct growth patterns based on the meteorological regime and surface type. Rimed particles appear more frequently during MCAOs, particularly over open ocean where increased turbulence likely promotes droplet–ice interactions. Over sea ice, pristine and aggregated crystals are more prevalent, implying that riming plays a lesser role while depositional growth and aggregation are more significant. This combined multi-campaign dataset offers a comprehensive statistical view on the interaction of synoptic regime, temperature inversion, and surface conditions in determining the microphysical structure and ice crystal habits of Arctic MPCs.