Objectives: Predicting pathological types in patients with adenocarcinoma and squamous carcinoma using CT perfusion imaging parameters based on brain metastasis lesions from lung cancer. Methods: We retrospectively studied adenocarcinoma and squamous carcinoma patients with brain metastases who received treatment and had been pathologically tested in our hospital from 2019 to 2021. CT perfusion images of the brain were used to segment enhancing tumors and peritumoral edema and to extract CT perfusion parameters. The most relevant perfusion parameters were identified to classify the pathological types. Of the 45 patients in the study cohort (mean age 65.64 ± 10.08 years; M:F = 24:21), 16 were found to have squamous cell carcinoma. Twenty patients were with brain metastases only, and 25 patients were found to have multiple organ metastases in addition to brain metastases. After admission, all patients were subjected to the CT perfusion imaging examination. Differences in CT perfusion parameters between adenocarcinoma and squamous carcinoma were analyzed. The receiver operating characteristic (ROC) curves were used to predict the types of pathology of the patients. Results: Among the perfusion parameters, cerebral blood flow (CBF) and mean transit time (MTT) were significantly different between the two lung cancers (adenocarcinoma vs. squamous cell carcinoma: p < 0.001, p = 0.012.). Gender and tumor location were identified as the clinical predictive factors. For the classification of adenocarcinoma and squamous carcinoma, the model combined with CBF and clinical predictive factors showed better performance [area under the curve (AUC): 0.918, 95% confidence interval (CI): 0.797-0.979). The multiple organ metastasis model showed better performance than the brain metastasis alone model in subgroup analyses (AUC: 0.958, 95% CI: 0.794-0.999). Conclusion: CT perfusion parameter analysis of brain metastases in patients with primary lung cancer could be used to classify adenocarcinoma and squamous carcinoma.