Seismic structural interpretation is a discipline of structural geology that uses seismic reflection data to interpret geologic structures in the subsurface. The advancement of machine learning technology, especially in image analysis methods like computer vision, has opened new frontiers for the discipline. Here, we review the current state of machine learning applied to seismic structural interpretation, outlining the philosophy, progress, pitfalls, and potential of the technology when applied to the characterization of subsurface structures. Seismic structural interpretation incorporates seismic image analysis, analogue use, and structural modeling to interpret geologic structures in the subsurface; machine learning approaches seek to supplement these current methods with workflows that benefit from a computer’s ability to continually improve with more data. Most of the current research in the discipline is focused on using convolutional neural networks (CNNs), a computer vision approach, to interpret faults, salt bodies, and structural traps. Automated structural trap interpretation presents unique challenges to the CNN approach, which we propose are best addressed using a geometric invariance-enforced deep learning method presented here. The CNNs and other computer vision methods offer performance advantages to seismic attribute-based approaches to structural interpretation, but still suffer from the potential pitfalls of image-only interpretation, like lacking geologic context. Additional challenges remain around constructing global, open training data, and developing new artificial intelligence approaches that can incorporate kinematic or geomechanical structural models in seismic interpretation workflows.