BackgroundLung cancer remains a major global health threat, with its biological complexity and patient heterogeneity posing significant challenges. Novel machine learning approaches now offer effective tools to interpret complex biological information hierarchies, showing promise to transform lung cancer treatment approaches.MethodsWe analyzed comprehensive biological datasets from TCGA and other databases, integrating DNA, RNA, miRNA, protein, and metabolite information. Multiple machine learning methods were employed to build diagnostic tools, treatment response predictors, and survival estimation models.ResultsOur machine learning approaches effectively distinguished cancer patients from healthy controls. Analysis identified unique molecular characteristics between lung cancer subtypes and discovered biomarkers that help predict treatment efficacy and patient prognosis. Adding clinical data to biological information significantly improved model accuracy and enhanced patient stratification.ConclusionThis study marks significant progress toward precision cancer therapy by demonstrating how machine learning can help decode the complex biology of lung cancer.