Lung cancer (LC) is one of the most frequently diagnosed cancers and remains the leading cause of cancer-related  mortality  worldwide,  representing  a  significant  global  health  challenge.  While numerous common lung diseases (CLDs) are implicated in LC development, the underlying causes of  LC  originating  from  CLDs  remain  inadequately  elucidated.  A  thorough  exploration  of  LC’s progression  from  CLDs  is  essential;  our  approach  integrated  bioinformatics  and  machine  learning, utilizing  data  from  GEO  and  TCGA  databases.  We  began  by  identifying  differentially  expressed genes (DEGs) in LC and CLDs, and our gene-disease network revealed for the first time shared DEGs (LC shares significant genes with TB (36), asthma (10), pneumonia (17), COPD (18), and Idiopathic Pulmonary Fibrosis (IPF) (78)), providing insights into potential connections of LC with CLDs. This analysis  not  only  broadened  our  understanding  of  their  associations  but  also  identified  significant pathways  and  hub  proteins  (SPTBN1,  KCNA4,  SCN7A,  KCNQ3,  GRIA1,  and  SDC1)  through  a protein-protein  interaction  network  (PPI).  Furthermore,  RNA-seq  and  clinical  data  were  obtained from the cBioPortal portal for shared DEGs of LC and CLDs, assessing their impact on LC patient survival. Integrated mRNA-Seq and clinical data were analyzed via univariate and multivariate Cox Proportional Hazard models to elucidate the influence of significant genes on survival. Furthermore, we  developed  and  deployed  a  predictive  model  leveraging  the  identified  hub  genes,  which demonstrated high accuracy in predicting LC progression. The identified biomarkers and pathways hold   promise   for   further   translational   research   and   potential   therapeutic   targets,   advancing understanding of LC development from CLDs. Additionally, co-expression networks among common genes were explored using the Weighted Gene Co-expression Network Analysis (WGCNA). Finally, the hub genes were validated using the Human Protein Atlas (HPA) database and evaluated through various classification algorithms to ascertain their predictive power and diagnostic potential