<p>Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality globally, with lung metastasis significantly impacting patient prognosis. Identifying predictors of lung metastasis is crucial for improving patient outcomes. This study utilizes data from the Surveillance Epidemiology and End Results (SEER) database, focusing on a cohort of 13,441 HCC patients. We examined various demographic and clinical factors, employing logistic regression and nomogram analysis to develop a predictive model for lung metastasis. The model’s effectiveness was validated using receiver operating characteristic (ROC) curve and calibration analyses. Key factors such as tumor grade, size, stage, and lymph node stage were identified as significant predictors of lung metastasis. The predictive model demonstrated high accuracy, with area under the ROC curve (AUC) values of 0.803 in the training set and 0.783 in the validation set. Calibration plots confirmed the model’s reliability in predicting lung metastasis. Our predictive model provides a robust tool for risk stratification and therapeutic planning in HCC patients, aiding clinical decision-making. This study highlights the potential of data-driven approaches in enhancing patient care and advancing HCC management.</p>