Background：Clinical laboratory data, indicative of tumor cell growth and metabolic activity, warrants investigation for its potential in predicting lung cancer metastasis. Aims: The purpose is to develop a predictive model for regional lymph node involvement and skip metastasis in lung cancer using machine learning methods and integrating clinical laboratory information and patient characteristics. Methods: Data from lung cancer patients at Chongqing University Fuling Hospital between January 2020 and December 2022 were analyzed retrospectively. Patients were divided into N (regional lymph node involvement prediction) and M (skip metastasis prediction) groups based on TNM staging criteria. Prognostic factors were determined through univariate analysis and LASSO regression, and machine learning algorithms were used to develop predictive models. Results: Out of a total of 1629 cases analyzed, 861 were in the N group and 519 were in the M group. Univariate analysis revealed 40 parameters that were significantly different between the two groups (p < 0.05) and 27 parameters, respectively. LASSO regression identified 13 characteristic factors for the N group and 12 for the M group. In the N group, these factors included tumor size, prothrombin time (PT), mean platelet volume, fibrinogen, platelet count, procalcitonin, carbohydrate antigen 15-3 (CA 15-3), carcinoembryonic antigen (CEA), adenosine deaminase, red blood cell distribution width, thrombin time, smoking and drinking history. In the M group, the factors were cytokeratin 19 fragment, tumor size, CEA, CA 15-3, squamous cell carcinoma-related antigen, alkaline phosphatase, fibrinogen, hemoglobin, calcium, albumin, PT, and absolute monocyte value. The test set results indicated that the Logistic regression model was optimal for both groups, achieving AUCs of 0.888 and 0.875, respectively. Conclusion: This study demonstrates the potential of using machine learning algorithms alongside clinical characteristics and laboratory data to predict regional lymph node involvement and skip metastasis in lung cancer.