A multimodal method comprising optical imaging using OCT and molecular detection using Raman spectroscopy was developed to explore its capability for noninvasive differentiation between melanoma skin cancer and benign skin lesions. Key OCT parameters like the attenuation coefficient, R , and RMSE, extracted through exponential fitting, were incorporated into machine learning, achieving 96.9% accuracy and an AUC-ROC of 0.99 in 10-fold cross-validation. Raman spectroscopy revealed differences in carotenoid, amide-I, and CH-CH structures between melanoma and nevi, supporting the OCT findings. Autofluorescence background intensity variations further distinguished lesion types and enhanced lesion assessment. Future work will include the investigation of larger patient groups and the combination of both data sets in a combined algorithm. Also, the integration of both modalities and the developed method with photoacoustic tomography and high-frequency ultrasound appears beneficial toward achieving an optical biopsy of melanoma skin cancer and improving diagnostics.