This study investigated the potential of combining baseline ¹⁸ F-FDG PET tumor-to-liver ratio radiomics with demographic data, using machine learning, to classify lymphoma subtypes and differentiate between candidates for ABVD and R-CHOP therapy. Additionally, we assessed whether nodal radiomics alone is sufficient for treatment and subtype classification. We conducted a multi-center study involving 241 lymphoma patients, including 125 with Non-Hodgkin lymphoma (NHL) and 116 with Hodgkin lymphoma. Among these, 94 had high-grade NHL, whereas 110 had classical Hodgkin lymphoma. We utilized 107 radiomic features, along with demographic data, such as age, stage, gender, and weight, to develop predictive models for classifying lymphoma subtypes and selecting treatment regimens (ABVD vs. R-CHOP). Data harmonization was performed using ComBat, feature selection was done with SelectKBest, and three machine learning models (Logistic Regression, Random Forest, and XGBoost) were trained with hyperparameter tuning, followed by external validation. For the best model in each classifier on the external test, adding extra-nodal radiomic features improved performance for certain lymphoma subtypes. For NHL vs. HL, accuracy increased from 0.807–0.819, whereas NHL precision rose from 0.837–0.875. High-grade NHL precision improved notably from 0.821–0.962. In treatment classification, extra-nodal features boosted accuracy for R-CHOP from 0.783–0.839 and increased F1-scores for both R-CHOP and ABVD. This study demonstrated the promise of PET radiomics combined with demographic features for lymphoma classification and treatment decision-making. Overall, extra-nodal features enhanced high-grade NHL and treatment classification but had minimal impact on other lymphoma subtypes.