Particle-resolved direct numerical simulations (PR-DNS) are crucial for unraveling the intricate interplay of aerosol-cloud-turbulence processes. However, such models are challenged by the huge computational cost due to the extremely high resolution. Our prior work showcased that leveraging machine learning emulators could slash computational expenses by two orders of magnitude while maintaining remarkable precision for dynamic fields, and exhibited generalizability across diverse initial conditions and at super-resolution scales without retraining the emulators. Building upon this foundation, this work extends the emulator’s application to thermodynamic in the two spatial dimensions and droplet fields in the three spatial dimensions. Furthermore, to enhance the robust generalizability of the emulator for different initial values and super resolution, we introduce a novel multi-initial learning approach for the neural operator method. For the droplet fields, we introduce a novel loss function tailored to assess distribution differences using the Mallows distance, focusing particularly on droplet size distributions. Our findings indicate that the machine learning emulators hold promising potential to effectively mimic numerical PR-DNS simulations, thereby significantly advancing our understanding of the complex interactions within aerosol-cloud-turbulence processes.