Grain morphology has significant impacts on the mechanical behaviors of granular materials. However, its influences on grain breakage are still poorly understood due to the complexity of morphological characterization. This work employs the machine learning (ML) approach to reveal the impacts of grain morphology on grain crushing strength. A database consisting of 400 high-resolution rock grains was collected by 3D scanning. The morphology of each grain is characterized using 24 global shape descriptors and the local roundness at the contact zone. Based on hierarchical clustering, eight descriptors are selected as input features to the ML model. Grain instability is also quantified and used in the training of the ML model. To eliminate the influence of material properties and size effects on grain breakage, we perform the combined finite and discrete element method simulation of the single grain crushing test to get the crushing strength, which is used as the output of the ML model. Among the five ML regression algorithms used, XGBoost has more outstanding prediction accuracy and generalization ability. Each feature's influence on crushing strength is then analyzed using permutation feature importance and SHapley Additive exPlanations (SHAP) value. The feature importance ranking indicates that grain instability has the most significant impact on the crushing strength. The SHAP value demonstrates that flat, rounded, and convex grains have higher resistance to compression. The local roundness at the contact points also affects the crushing strength, in which higher local roundness corresponds to larger crushing strength. The results revealed by ML are in agreement with previous qualitative studies. The interpretable ML may shed new light on the investigation of the complex granular materials.