Pain after transcatheter arterial chemoembolisation (TACE) can seriously affect the prognosis of patients and the insertion of additional medical resources.To develop an early warning model for predicting pain after TACE to enable the implementation of preventive analgesic measures.We retrospectively collected the clinical data of 857 patients (from January 2016 to January 2020) and prospectively enrolled 368 patients (from February 2020 to October 2022; as verification cohort) with hepatocellular carcinoma (HCC) who received TACE in the Hepatic Surgery Center of Tongji Hospital. Five predictive models were established using machine learning algorithms, namely, random forest model (RFM), support vector machine model, artificial neural network model, naive Bayes model and decision tree model. The efficacy of these models in predicting postoperative pain was evaluated through receiver operating characteristic curve analysis, decision curve analysis and clinical impact curve analysis.A total of 24 candidate variables were included in the predictive models using the iterative algorithms. Age, preoperative pain, number of embolised tumours, distance from the liver capsule, dosage of iodised oil and preoperative prothrombin activity were closely associated with postoperative pain. The accuracy of the predictive model was compared between the training [area under the curve (AUC) = 0.798; 95% confidence interval (CI): 0.745-0.851] and verification (AUC = 0.871; 95%CI: 0.818-0.924) cohorts, with RFM having the best predictive efficiency (training cohort: AUC = 0.869, 95%CI: 0.816-0.922; internal verification cohort: AUC = 0.871; 95%CI: 0.818-0.924).The five predictive models based on advanced machine learning algorithms, especially RFM, can accurately predict the risk of pain after TACE in patients with HCC. RFM can be used to assess the risk of pain for facilitating preventive treatment and improving the prognosis.©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.