Abstract Mixing cations has been a successful strategy in perovskite synthesis by solution-processing, delivering improvements in the thermodynamic stability as well as in the lattice parameter control. Unfortunately, the relation between a given cation mixture and the associated structural deformation is not well-established, a fact that hinders an adequate identification of the optimum chemical compositions. Such difficulty arises since local distortion and microscopic disorder influence structural stability and also determine phase segregation. Hence, the search for an optimum composition is currently based on experimental trial and error, a tedious and high-cost process. Here, we report on a machine-learning-reinforced cubic-phase-perovskite stability predictor that has been constructed over an extensive dataset of first-principles calculations. Such a predictor allows us to determine the cubic phase stability at a given cation mixture regardless of the various cations’ pair and concentration, even assessing very dilute concentrations, a notoriously challenging task for first-principles calculations. In particular, we construct machine learning models, predicting multiple target quantities such as the enthalpy of mixing and various octahedral distortions. It is then the combination of these targets that guide the laboratory synthesis. Our theoretical analysis is also validated by the experimental synthesis and characterization of methylammonium–dimethylammonium-mixed perovskite thin films, demonstrating the ability of the stability predictor to drive the chemical design of this class of materials.