Accurate and efficient prediction of the distribution of surface loads on buildings subjected to explosive effects is crucial for rapidly calculating structural dynamic responses, establishing effective protective measures, and designing civil defense engineering solutions. Current state-of-the-art methods face several issues: Experimental research is difficult and costly to implement, theoretical research is limited to simple geometries and lacks precision, and direct simulations require substantial computational resources. To address these challenges, this paper presents a data-driven method for predicting blast loads on building surfaces. This approach increases both the accuracy and computational efficiency of load predictions when the geometry of the building changes while the explosive yield remains constant, significantly improving its applicability in complex scenarios. This study introduces an innovative encoder–decoder graph neural network model named BlastGraphNet, which uses a message-passing mechanism to predict the overpressure and impulse load distributions on buildings with conventional and complex geometries during explosive events. The model also facilitates related downstream applications, such as damage mode identification and rapid assessment of virtual city explosions. The calculation results indicate that the prediction error of the model for conventional building tests is less than 2%, and its inference speed is 3–4 orders of magnitude faster than that of state-of-the-art numerical methods. In extreme test cases involving buildings with complex geometries and building clusters, the method achieved high accuracy and excellent generalizability. The strong adaptability and generalizability of BlastGraphNet confirm that this novel method enables precise real-time prediction of blast loads and provides a new paradigm for damage assessment in protective engineering.