Lung cancer (LC) is a major cause of mortality among malignant tumors. Early diagnosis through lipidomic profiling can improve prognostic outcomes. In this study, a uniform PbS/Au-layered substrate that enhances the laser desorption/ionization process, an interfacial process triggered on the substrate surface upon laser excitation, was designed to efficiently characterize the lipidomic profiles of LC patient serum. By controlling the stacking arrangement and particle sizes of PbS QDs and AuNPs, the optimized substrate promotes the generation of excited electrons and creates an enhanced electric field that polarizes analyte molecules, facilitating ion adduction formation ([M + Na] and [M + K]) and enhancing detection sensitivity down to the femtomole level. Combining multivariate statistics and machine learning, a distinct lipidomic biomarker panel is successfully identified for the early diagnosis and staging of LC, with an accurate prediction validated by an area under the curve of 0.9479 and 0.9034, respectively. We also found that 18 biomarkers were significantly correlated with six metabolic pathways associated with LC. These results demonstrate the potential of this innovative PbS/Au-layered substrate as a sensitive platform for accurate diagnosis of LC and facilitate the development of lipidomic-based diagnostic tools for other cancers.