Accurate and early diagnosis of lung cancer is critical for effective treatment and improved patient outcomes. Traditional diagnostic methods face challenges in sensitivity and specificity, particularly for multi-biomarker detection in complex biological samples. This study introduces a novel platform combining Surface-Enhanced Raman Spectroscopy (SERS) with machine learning techniques for simultaneous multi-biomarker detection and quantification. The developed SERSIA platform leverages gold nanoparticle-based substrates and advanced classification algorithms (t-SNE, SVM) to achieve high sensitivity and specificity. Validation studies on human serum samples revealed that the platform could accurately detect and quantify four key lung cancer biomarkers—CYFRA21-1, CEA, SCC-ag, and GCC2—achieving 92% diagnostic accuracy. Moreover, the method enabled precise differentiation of cancer subtypes and stages with over 82% accuracy. This study underscores the transformative potential of integrating SERS and machine learning in advancing precision diagnostics, paving the way for broader clinical applications in early cancer detection and personalized medicine.