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Abstract Structural domains and domain walls, inherent in single crystalline perovskite oxides, can significantly influence the properties of the material and therefore must be considered as a vital part of the design of the epitaxial oxide thin films. We employ 4D-STEM combined with machine learning (ML) to comprehensively characterize domain structures at both high spatial resolution and over a significant spatial extent. Using orthorhombic LaFeO₃as a model system, we explore the application of unsupervised and supervised ML in domain mapping, which demonstrates robustness against experiment uncertainties. The results reveal the consequential formation of multiple domains due to the structural degeneracy when LaFeO₃film is grown on cubic SrTiO₃. In situ annealing of the film shows the mechanism of domain coarsening that potentially links to phase transition of LaFeO₃at high temperatures. Moreover, synthesis of LaFeO₃on DyScO₃illustrates that a less symmetric orthorhombic substrate inhibits the formation of domain walls, thereby contributing to the mitigation of structural degeneracy. High fidelity of our approach also highlights the potential for the domain mapping of other complicated materials and thin films.