Skin cancer is the most prevalent cancer, and its assessment remains a challenge for physicians. This study reports the application of an optical sensing method, elastic scattering spectroscopy (ESS), coupled with a classifier that was developed with machine learning, to assist in the discrimination of skin lesions that are concerning for malignancy. The method requires no special skin preparation, is non‐invasive, easy to administer with minimal training, and allows rapid lesion classification. This novel approach was tested for all common forms of skin cancer. ESS spectra from a total of 1307 lesions were analyzed in a multi‐center, non‐randomized clinical trial. The classification algorithm was developed on a 950‐lesion training dataset, and its diagnostic performance was evaluated against a 357‐lesion testing dataset that was independent of the training dataset. The observed sensitivity was 100% (14/14) for melanoma and 94% (105/112) for non‐melanoma skin cancer. The overall observed specificity was 36% (84/231). ESS has potential, as an adjunctive assessment tool, to assist physicians to differentiate between common benign and malignant skin lesions. This study reports the application of elastic scattering spectroscopy coupled with machine learning to assist in the discrimination of skin lesions that are concerning for malignancy. The method requires no special skin preparation, is non‐invasive, requires minimal training, and allows rapid lesion classification. The classification algorithm was developed on a 950‐lesion training dataset, and diagnostic performance was evaluated against a 357‐lesion testing dataset. The observed sensitivity was 100% (14/14) for melanoma and 94% (105/112) for nonmelanoma skin cancer. The overall specificity was 36% (84/231). ESS has potential to assist physicians in evaluating common benign and malignant skin lesions.