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Small changes in seismic wave properties foretell frictional failure in laboratory experiments and in some cases on seismic faults. Such precursors include systematic changes in wave velocity and amplitude throughout the seismic cycle. However, the relationships between wave features and shear stress are complex. Here, we use data from lab friction experiments that include continuous measurement of elastic waves traversing the fault and build data‐driven models to learn these complex relations. We demonstrate that deep learning models accurately predict the timing and size of laboratory earthquakes based on wave features. Additionally, the transportability of models is explored by using data from different experiments. Our deep learning models transfer well to unseen datasets providing high‐fidelity models with much less training. These prediction methods can be potentially applied in the field for earthquake early warning in conjunction with long‐term time‐lapse seismic monitoring of crustal faults, CO2 storage sites and unconventional energy reservoirs. Plain Language Summary: Laboratory experiments and field observations show that wave velocity, amplitude and frequency vary systematically over time during seismic cycles. These wave characteristics drop before failure (shear stress drop) albeit at different times and thus are believed to contain precursory information about the upcoming failure event. Here, we continuously record ultrasonic data during a series of experiments designed to simulate earthquakes in the laboratory or laboratory quakes. We investigate whether machine learning can predict the occurrence of laboratory quakes from ultrasonic data. We apply XGBoost and a suite of deep learning methods to this data and present models that can accurately predict the laboratory quake timing, size or both. We compare the performance of different models in terms of accuracy and training time. Also, we interpret the developed models. Finally, we show that these models successfully transfer from one data set to another obtained using different experimental constraints. Consequently, the training time and amount of data necessary to develop models for new datasets are significantly reduced. The developed prediction models can be used for seismic hazard assessment and warning, safe management of CO2 storage sites and unconventional energy reservoirs in conjunction with continuous and long‐term seismic monitoring. Key Points: We predict size and timing of lab quakes from active source ultrasonic data and deep learning models yield the most accurate predictionsPredictions are accurate despite irregular seismic cyclesDeep learning models transfer better to other datasets; transfer learning reduces the training time and size of training data [ABSTRACT FROM AUTHOR]