Researchers are exploring the use of machine learning trained on surrogates of historical data to predict critical transitions. Studies have applied this approach to various fields, including quantum processors, fluid-solid interaction models, and intensive care units. The technique has been demonstrated in multiple case studies, including predicting bifurcations, proppant dynamics, and tipping points in different systems.