Background: Current TNM staging systems insufficiently predict individual prognosis and chemotherapy response in gastric cancer (GC). We aimed to develop and validate a radiopathomics signature (RPS) integrating computed tomography (CT) and pathological features to improve prognostic stratification and guide treatment decisions. A preliminary evaluation of the immunotherapy response was also conducted. Methods: This retrospective multicenter analysis included 1133 GC patients across three Chinese institutions. We integrated features extracted from CT images and H&E stain slides using ResNet-50 and HoverNet, encompassing radiomics, pathomics microenvironment, single-cell spatial distribution, and pathomics nucleus data, to develop the RPS. Prognostic performance was evaluated using the area under the time-dependent curve (AUC) and C-index. Chemotherapy and immunotherapy benefits were determined using Kaplan–Meier analysis. Results: The RPS demonstrated strong performance in predicting 5-year overall survival (OS), with AUCs of 0.928 (95% CI: 0.899–0.956) in the training cohort and 0.857–0.917 in internal and external validation cohorts, showing improved prognostic accuracy compared with single-modality radiomics and pathomics models. The model can identify patients who could benefit from postoperative chemotherapy (HR: 11.751, P < 0.0001). Moreover, the RPS showed a preliminary but significant association with treatment response in the immunotherapy cohort ( n = 64; HR: 3.651, P = 0.009). The nomogram combining RPS and TNM stage yielded good C-indexes of 0.79–0.84 for OS across cohorts. Conclusions: The RPS robustly predicts prognosis and chemotherapy benefit in GC patients and provides preliminary insights into immunotherapy response prediction, complementing the TNM staging system and helping to guide patient-specific treatment strategies.