This study presents a comprehensive machine learning approach that integrates clinical data, CT-derived morphological indicators, and radiomic signatures to enable the non-invasive prediction of EGFR mutation status in individuals diagnosed with non-small cell lung cancer (NSCLC). The gene discussed is EGFR; the disease is non-small cell lung carcinoma.