Abstract: Prefabricated building structures have been widely applied in civil engineering due to their advantages of energy conservation, environmental protection, controllable quality, and efficient construction. As the core load-bearing components of prefabricated building structures, precast reinforced concrete slabs (PC slabs) are vulnerable to threats from gas explosions, industrial explosions, and terrorist attacks. To accurately assess the damage state of PC slabs under explosion, enhance structural blast resistance, and reduce casualties, this paper constructs an explosion response dataset of PC slabs. Six geometric parameters (slab thickness, span, reinforcement ratio, etc.) and two explosion load parameters (peak pressure, impulse) are selected as input features. Three machine learning algorithms (GPR, RF, and XGBoost) are used to predict the maximum displacement of PC slabs, and their prediction accuracies are compared by Mean Absolute Error （<italic>RMSE</italic>）,Coefficient of Determination（<italic>R²</italic>）, Mean Absolute Percentage Error（<italic>MAE</italic>）, Scattering Index（<italic>SI</italic>）, and Comprehensive Performance ObjectiveFunction（<italic>OBJ</italic>）. Furthermore, a damage classification evaluation model based on the support rotation angle damage criterion is proposed. The performance differences of the model under three criteria are analyzed by confusion matrix and five classification indices (<italic>Accuracy</italic>, <italic>Precision</italic>, <italic>Rrecall</italic>, <italic>F1-score</italic>, and Kappa coefficient), and compared with simplified models and empirical prediction methods. The research results indicate that in terms of maximum displacement prediction for PC slabs under explosion loads, the XGBoost model demonstrates the best performance among the three machine learning models（GPR、RF and XGBoost）. Specifically, its fitting degree is superior to that of GPR and RF models, and it shows the most outstanding comprehensive performance, with a damage recognition accuracy of 92.5 %, which fully demonstrates its high-efficiency in identifying different damage types. The XGBoost-based damage classification evaluation model for PC slabs under explosion loads exhibits strong performance, providing important references for structural blast resistance design and rapid post-blast damage assessment.