This study develops a data-driven modeling pipeline to predict the mechanical behavior of adherent cells using images of cytoskeletal proteins. The researchers trained neural networks to predict cellular forces from images of zyxin, a focal adhesion (FA) protein, and found that a single FA protein is sufficient to predict traction stresses. This model generalizes to different cell types, experimental setups, and biomechanical regimes. The study introduces three data-driven approaches: a physical bottleneck neural network (PBNN) that enhances existing models, a Green’s function neural network (GFNN) that reveals long-range interactions, and an analytical formula that describes the system behavior. These methods uncover two important length scales: one on the order of microns for local FA intensity and another on the order of tens of microns for cell morphology. The findings demonstrate the potential of machine learning in extracting robust predictions from heterogeneous biological data and advancing our understanding of cell mechanics.This study develops a data-driven modeling pipeline to predict the mechanical behavior of adherent cells using images of cytoskeletal proteins. The researchers trained neural networks to predict cellular forces from images of zyxin, a focal adhesion (FA) protein, and found that a single FA protein is sufficient to predict traction stresses. This model generalizes to different cell types, experimental setups, and biomechanical regimes. The study introduces three data-driven approaches: a physical bottleneck neural network (PBNN) that enhances existing models, a Green’s function neural network (GFNN) that reveals long-range interactions, and an analytical formula that describes the system behavior. These methods uncover two important length scales: one on the order of microns for local FA intensity and another on the order of tens of microns for cell morphology. The findings demonstrate the potential of machine learning in extracting robust predictions from heterogeneous biological data and advancing our understanding of cell mechanics.