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Abstract Background A considerable portion of patients with fever of unknown origin (FUO) present concomitant lymphadenopathy. Diseases within the spectrum of FUO accompanied by lymphadenopathy include lymphoma, infections, and rheumatic diseases. Particularly, lymphoma has emerged as the most prevalent etiology of FUO with associated lymphadenopathy. Distinguishing between benign and malignant lymph node lesions is a major challenge for physicians and an urgent clinical concern for patients. However, conventional imaging techniques, including PET/CT, often have difficulty accurately distinguishing between malignant and benign lymph node lesions. This study utilizes PET/CT radiomics to differentiate between lymphoma and benign lymph node lesions in patients with FUO, aiming to improve diagnostic accuracy. Results Data were collected from 204 patients who underwent 18F-FDG PET/CT examinations for FUO, including 114 lymphoma patients and 90 patients with benign lymph node lesions. Patients were randomly divided into training and testing groups at a ratio of 7:3. A total of 15 effective features were obtained by the least absolute shrinkage and selection operator (LASSO) algorithm. Machine learning models were constructed using logistic regression (LR), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) algorithms. In the training group, the area under the curve (AUC) values for predicting lymphoma and benign cases by LR, SVM, RF, and KNN models were 0.936, 0.930, 0.998, and 0.938, respectively. There were statistically significant differences in AUC between the RF and other models (all P 0.05). The decision curve analysis (DCA) curves of the RF model outperformed those of the other three models in both the training and testing groups. Conclusions PET/CT radiomics demonstrated promising performance in discriminating lymphoma from benign lymph node lesions in patients with FUO, with the RF model showing the best performance.