Objective This study aimed to establish an interpretable predictive model for assessing the risk of lung cancer occurrence using human blood fatty acid levels thereby promoting the prevention and early diagnosis of lung cancer. Method Clinical and blood fatty acid data from 1 837 patients with lung cancer 1194 cases or non - tumorous 643 cases conditions were retrospectively collected from June 2023 to June 2025 at the First Medical Center 1677 cases and the Eighth Medical Center 160 cases of the Chinese PLA General Hospital. By setting a random number seed the dataset from the First Medical Center of the PLA General Hospital was randomly split into a training set and an internal validation set at a ratio of 7 ∶ 3 while the dataset from the Eighth Medical Center was designated as the external validation set. Five models—random forest extreme gradient boosting support vector machine SVM classification and regression tree and logistic regression—were developed to predict the risk of lung cancer. The predictive performance of the five models was compared using the area under the receiver operating characteristic curve decision curve analysis and calibration curves and the optimal model was selected. The SHapley additive explanations method was used to interpret the SVM model. Result A clinical prediction model was established incorporating 17 predictors including age palmitic acid palmitoleic acid linoleic acid γ - linolenic acid α - linolenic acid arachidonic acid eicosapentaenoic acid docosahexaenoic acid total monounsaturated fatty acids total polyunsaturated fatty acids the ratio of AA to eicosapentaenoic acid and the ratio of ω - 6 PUFAs to ω - 3 PUFAs. The external validation results showed that the area under the receiver operating characteristic curve of the Random Forest model Extreme Gradient Boosting model SVM model Classification and Regression Tree model and Logistic Regression model were 0. 927 0. 931 0. 934 0. 84 and 0. 912 respectively. Decision curve analysis indicated that across most probability thresholds the SVM model tended to yield greater net benefit compared to the other models. Conclusion The lung cancer risk prediction model constructed based on the SVM model exhibited the best performance. It can effectively assist medical personnel in early identification of high-risk populations for lung cancer and facilitate precise nutritional interventions targeting risk factors to reduce the incidence of the disease.