Abstract: Scour can transfer the seismic vulnerable portions of a bridge from piers to pile-group foundations, which increases the cost and difficulty of implementing the conventional capacity-protection design strategy for pile-group foundations. Therefore, leveraging the energy dissipation of pile-group foundations becomes a potential alternative strategy. The seismic energy dissipation mechanisms of scoured bridge pile-group foundations are mainly categorized into three types, i.e., rocking-induced energy dissipation, plasticity-induced energy dissipation, and the energy dissipation induced by both the rocking and plastic behaviors. For pile-group foundations with different soil properties, scour depths, and structural parameters, accurate and efficient identification of their seismic energy dissipation mechanisms is a premise for the seismic design of scoured bridges. However, the conventional nonlinear seismic behavior analysis of pile-group foundations relies on complex finite element simulations frequently with computational convergence problems. In this study, a machine learning-driven method is proposed for efficient identification and analysis of the seismic energy dissipation mechanism of scoured bridge pile-group foundations in cohesionless soils. A dataset for seismic energy dissipation mechanism of scoured bridge pile-group foundations is established by using the experimentally validated finite element analysis method and by random sampling technique. The optimized support vector machine, neural network, and ensemble tree algorithms are used to establish a machine learning-driven model for efficient identification and analysis of seismic energy dissipation mechanisms. The study results show that the neural network can more accurately identify the seismic energy dissipation mechanism of scoured bridge pile-group foundations with both the precision and recall basically larger than 90%. The number of pile rows, of pile length, of pile longitudinal reinforcement ratio, of pier height and, of pile axial load ratio are important variables for the identification of the seismic energy dissipation mechanism of scoured pile-group foundations. The smaller the number of pile rows, of pile length and, of pile axial load ratio, and the larger the pile longitudinal reinforcement ratio and pier height, the more the scoured pile-group foundation tends to consume energy by the rocking behavior, and conversely, the more it tends to dissipate energy by the structural plasticity. For easy implementation, the dataset and neural network established in this study are publicly available.