Glioma is the most common tumor of the central nervous system (CNS). Drug resistance, and lack of effective treatment methods make the treatment effect of glioma patients unsatisfactory. The recent discovery of cuproptosis has led to new thinking about the therapeutic and prognostic targets of glioma. The transcripts and clinical data of glioma samples were obtained from The cancer genome atlas (TCGA). The cuproptosis-related lncRNA (CRL)-based glioma prognostic models were built through least absolute shrinkage and selection operator (LASSO) regression analysis in the train set and validated in the test set. Kaplan-Meier survival curve, risk curve analysis, and time-dependent receiver operating characteristic (ROC) curve were used to assess the predictive ability and risk differentiation ability of the models. Univariate and multivariate COX regression analyses were conducted on the models and various clinical features, and then nomograms were constructed to verify their predictive efficacy and accuracy. Finally, we explored potential associations of the models with immune function, drug sensitivity, and the tumor mutational burden of glioma. Four CRLs were selected from the training set of 255 LGG samples and the other four CRLs were selected from the training set of 79 GBM samples to construct the models. Follow-up analysis showed that the models have commendable prognostic value and accuracy for glioma. Notably, the models were also associated with the immune function, drug sensitivity, and tumor mutational burden of gliomas. Our study showed that CRLs were prognostic biomarkers of glioma, closely related to glioma immune function. CRLs may affect uniquely the sensitivity of glioma treatment. It will be a potential therapeutic target for glioma. CRLs will offer new perspectives on the prognosis and therapy of gliomas.