Atmospheric aerosols play an important role in several key processes related to atmospheric chemistry and physics. However, to limit computational expense, current regional and global chemical transport models need to grossly simplify the representation of aerosols, thereby introducing errors and uncertainties in our estimates of aerosol impacts on climate. This work shows how machine learning (ML) can be used to aid modeling of atmospheric aerosol. We illustrate this with two applications that both use detailed particle-resolved simulations as a basis to generate training data. The first application shows how microscale process of particle coagulation can be learned directly from data. The second application shows how ML can be used to bridge from accurate fine-scale aerosol models to the global scale for the evaluation of climate impacts. We focus on the aerosol mixing state, which is an important emergent property that affects the aerosol radiative forcing and aerosol-cloud interactions. In conclusion, the integration of machine learning methodologies into atmospheric aerosol modeling presents a promising avenue, offering both enhanced microscale understanding through direct data learning and improved global-scale modeling, thereby paving the way for more accurate estimations of aerosol impacts on climate.