Lung cancer is a complex and prevalent disease requiring comprehensive treatment approaches, including surgical management. Predicting postoperative length of stay (LOS) has emerged as a crucial aspect of efficient patient management and resource allocation. This study aimed to predict postoperative LOS after surgical resection for lung cancer using demographic and clinical characteristics available in administrative data. We analyzed 323 patients records (out of 357) from 2019 to 2022, with a mean total LOS of 11.86 days and mean preoperative LOS of 1.04 days. Most cases (65.3%) had localized cancer, while 34.7% had non-localized cancer. We run a multiple linear regression model incorporating age, sex, preoperative LOS, cancer condensed staging, and severity of illness. The regression model demonstrated statistical significance in predicting postoperative LOS. Female sex and higher cancer condensed staging were associated with shorter LOS, while higher severity of illness correlated with longer LOS. These findings highlight the importance of accurately predicting postoperative LOS for optimizing patient flow and resource allocation in lung cancer surgery. Further research addressing limitations and exploring additional predictors is warranted to refine prediction models and improve patient outcomes.