We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individual induction chemotherapy (IC) in advanced nasopharyngeal carcinoma (NPC). We constructed radiomics signatures and nomogram for predicting disease-free survival (DFS) based on the extracted features from PET and CT images in a training set ( = 470), and then validated it on a test set ( = 237). Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were applied to evaluate the discriminatory ability of radiomics nomogram, and compare radiomics signatures with plasma Epstein-Barr virus (EBV) DNA. A total of 18 features were selected to construct CT-based and PET-based signatures, which were significantly associated with DFS ( < 0.001). Using these signatures, we proposed a radiomics nomogram with a C-index of 0.754 95% confidence interval (95% CI), 0.709-0.800 in the training set and 0.722 (95% CI, 0.652-0.792) in the test set. Consequently, 206 (29.1%) patients were stratified as high-risk group and the other 501 (70.9%) as low-risk group by the radiomics nomogram, and the corresponding 5-year DFS rates were 50.1% and 87.6%, respectively ( < 0.0001). High-risk patients could benefit from IC while the low-risk could not. Moreover, radiomics nomogram performed significantly better than the EBV DNA-based model (C-index: 0.754 vs. 0.675 in the training set and 0.722 vs. 0.671 in the test set) in risk stratification and guiding IC. Deep learning PET/CT-based radiomics could serve as a reliable and powerful tool for prognosis prediction and may act as a potential indicator for individual IC in advanced NPC.