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Designing magnesium sheet alloys for room temperature (RT) forming is a challenge due to the limited deformation modes offered by the hexagonal close-packed crystal structure of magnesium. To overcome this challenge for lightweight applications, critical understanding of alloying-processing–microstructure relationship in magnesium alloys is needed. In this work, machine learning (ML) algorithms have been used to fundamentally understand the alloying-processing–microstructure correlations for RT formability in magnesium alloys. Three databases built from 135 data collected from the literature were trained using 10 commonly used machine learning models. The accuracy of the model is obviously improved with the increase in the number of features. The ML results were analyzed using advanced SHapley Additive exPlanations (SHAP) technique, and the formability descriptors are ranked as follows: (1) microstructure: texture intensity > grain size; (2) annealing processing: time > temperature; and (3) alloying elements: Ca > Zn > Al > Mn > Gd > Ce > Y > Ag > Zr > Si > Sc > Li > Cu > Nd. Overall, the texture intensity, annealing time and alloying Ca are the most important factors which can be used as a guide for high-formability sheet magnesium alloy design.