Ice-shelf calving has a direct impact on Antarctic mass loss and dynamic processes, and it is particularly important to study its spatial characteristics, environmental conditions, and controlling factors. Based on the machine learning algorithms and ice sheet dynamic models, utilizing remote sensing data on Antarctic ice shelf calving from 2005 to 2020, ice shelf surface fracture data, ice shelf buttressing value, spatial distribution data of Antarctic ice shelf damage, and basal melting data, combined with machine learning binary classification, the importance of 18 characteristic elements influencing ice shelf dynamic processes was analyzed, and the accuracy of seven different machine learning algorithms was calculated. The results indicate that the random forest algorithm achieves the highest accuracy in the binary classification of ice shelf calving and that surface meltwater has a significant impact on ice shelf collapse, indicating the feasibility of using both the intrinsic dynamics of the ice shelf and external environmental factors for prediction. Subsequent efforts should further couple dynamic models with machine learning algorithms and establish corresponding numerical modeling systems to depict ice-shelf calving events with higher spatiotemporal resolutions in terms of intensity and extent.