Objective: To construct hybrid immune-radiomic (ImRad) phenotypes of clear cell renal cell carcinoma (ccRCC) based on ensemble machine learning (ML) and contrast-enhanced CT, and to investigate its predictive value for survival. Methods: The clinical, CT imaging and RNAseq information of 113 ccRCC patients were collected from TCGA database. Radiomic features were extracted from whole tumor. ImRad predictors were constructed on tumor immuneinfiltration, tumor mutational burden, immune exhaustion gene expression after feature selection based on ensemble ML. Further, the predictive value of ImRad for overall survival (OS) was assessed using multivariate Cox regression analysis. Results: Among the 30 ImRad contructed by ensemble ML and validated by 5 folds cross validation, Naive Bayes algorithm achieved the generally best performance (area under the receiver operating characteristic curve was from 0.724 to 0.956). The median OS of 113 patients was 31 (10 to 54) months. Clinicopathologic immune comprehensive model on predicting OS achieved best performance, surpassing single modality indicators based on clinical, pathological and ImRad (C index: 0.938, 0.756 and 0.924, respectively). Among ImRad features, RadMast_cells_activated was one of eight independent predictive factors for patients’ prognosis. Conclusions: CT radiomics based on ensemble ML can predict immune microenvironment and improve the prediction efficiency of postoperative survival of ccRCCs.