The aim of this study is to adopt machine learning and deep learning methods to predict the risk of post-GKS edema for meningiomas. 595 multicenter cases were included to train and validate 38 random survival forest (RSF) and DeepSurv models. The RSF model incorporating clinical, semantic, and ADC radiomic features achieved the best performance with a C-index of 0.861 in internal validation, and 0.780 in external validation. The derived nomogram had excellent discrimination and calibration. The proposed RSF model with a nomogram represents a non-invasive and cost-effective tool to predict post-GKS edema risks, thus facilitates personalized decision-making in meningioma treatment.