Abstract: Objective: To integrate clinical and pathological whole slide image (WSI) with magnetic resonance imaging (MRI) data to construct a multimodal machine learning model for assessing postoperative recurrence risk in nasopharyngeal carcinoma patients. Methods: Retrospective collection of clinical data, WSI, and multi-sequence MRI from 168 nasopharyngeal carcinoma patients. MRI features and tumor region features of WSI were extracted separately via radiomics and the CTransPath+CLAM framework, respectively. The performance of unimodal and multimodal prediction models was compared using the random forest method. All models were trained and evaluated via 5-fold stratified cross-validation. The area under the receiver operating characteristic curve (AUC) served as the primary performance metric, and clinical net benefit was assessed using decision curve analysis. Results: The multimodal model integrating clinical data, WSI, and MRI demonstrated the best predictive performance, with an AUC of 0.794, representing an improvement of 0.215 compared with the clinical indicators model (AUC=0.579, P=0.109) and an increase of 0.183 compared with the AJCC anatomic staging model (AUC=0.611, P=0.015); however, the combined model of clinical indicators and staging (AUC = 0.660) still showed a significant deficit compared with the multimodal model (ΔAUC=0.134, P=0.015). In head-to-head comparisons, the multimodal model also outperformed the MRI model (AUC=0.769, P＞0.05) and the WSI model (AUC=0.511, P＜0.001). Decision curve analysis (DCA) indicated that the multimodal model yielded the highest net benefit across most risk threshold ranges. Model interpretation revealed that its predictive power primarily stems from MRI textural features reflecting tumor heterogeneity. Conclusion: A multimodal machine learning model is successfully constructed and validated. By integrating clinical data, WSI and MRI information, it demonstrates promising clinical application potential for recurrence prediction of nasopharyngeal carcinoma.