Clouds continue to challenge our understanding and are major sources of uncertainty in numerical weather prediction and climate projection. Key to addressing such outstanding challenges associated with clouds are to understand the processes that occur at space-time scales of individual clouds or smaller, including entrainment/detrainment, turbulent mixing, turbulence, cloud microphysics, radiation, aerosol activation/deactivation, and boundary layer processes. These sub-grid cloud processes pose particular challenges to numerical simulations, observations, as well as parameterization development, where AI can help unravel process mechanisms and advance parameterization schemes efficiently. Representations of these sub-grid cloud processes will likely become more important as models increase their resolutions. This session focuses on process-level modeling, observations (both laboratory and field), AI-aided analysis  and parameterizations of cloud processes. Also invited are studies on unified treatment of different processes, process coupling, and integration from observations, theories to models.