Reliable prediction of solar transient events (flares, coronal mass ejections, energetic particle events, etc.) remains an important problem from both research and operational perspectives. Induced by the growing amounts of high-quality observational data and accessibility of machine learning (ML) techniques, the ML-driven approaches represent a viable path to forecast such events. This session aims to highlight the advances in ML-driven attempts for solar transient event forecasting, the challenges associated with them, and the fundamental physical knowledge derived by them. We welcome all contributions that focus on but are not limited to 1) application of novel ML algorithms and approaches, 2) data preparation and augmentation, 3) forecast assessment/evaluation, 4) class-imbalance treatment strategies, 5) forecast interpretation, and 6) forecast operationalization.

• NG - Nonlinear Geophysics