OBJECTIVE: This study aims to develop and evaluate a radiomics-based machine learning model using T1-enhanced magnetic resonance imaging (MRI) features to differentiate between lung squamous cell carcinoma (SCC) and adenocarcinoma (AC) in patients with brain metastases (BMs). While prior studies have largely focused on primary lung tumors, our work uniquely targets metastatic brain lesions, which pose distinct diagnostic and therapeutic challenges. METHODS: In this retrospective study, 173 patients with BMs from lung cancer were included, consisting of 88 with AC and 85 with SCC. MRI images were acquired using a standardized protocol, and 833 radiomic features were identified from the segmented lesions utilizing the PyRadiomics package. Feature selection was performed using a combination of univariate analysis, correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. Ten machine learning classifiers were trained and validated utilizing the selected features. The performance of the classifier models was assessed through receiver operating characteristic (ROC) curves, and the area under the curve (AUC) was examined for analysis. RESULTS: Ten classifier models were built on the basis of features derived from MRI. Among the ten classifier models, the LightGBM model performed the best. In the training dataset, the LightGBM classifier achieved an accuracy of 0.814, with a sensitivity of 0.726 and specificity of 0.896. The classifier's efficiency was validated on an independent testing dataset, where it maintained an accuracy of 0.779, with a sensitivity of 0.725 and specificity of 0.857. The AUC was 0.858 for the training dataset and 0.857 for the testing dataset. The model effectively distinguished between SCC and AC based on radiomic features, highlighting its potential for noninvasive non-small cell lung cancer (NSCLC) subtype classification. CONCLUSION: This research demonstrates the efficacy of a radiomics-based machine learning model in accurately classifying NSCLC subtypes from BMs, providing a valuable noninvasive tool for guiding personalized treatment strategies. Further validation on larger, multi-center datasets is crucial to verify these findings.