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Oral candidiasis, a common opportunistic infection of the oral cavity, is mainly caused by the following four <i>Candida</i> species (in decreasing incidence rate): <i>Candida albicans</i>, <i>Candida glabrata</i>, <i>Candida tropicalis</i>, and <i>Candida krusei</i>. This study offers in-depth Raman spectroscopy analyses of these species and proposes procedures for an accurate and rapid identification of oral yeast species. We first obtained average spectra for different <i>Candida</i> species and systematically analyzed them in order to decode structural differences among species at the molecular scale. Then, we searched for a statistical validation through a chemometric method based on principal component analysis (PCA). This method was found only partially capable to mechanistically distinguish among <i>Candida</i> species. We thus proposed a new Raman barcoding approach based on an algorithm that converts spectrally deconvoluted Raman sub-bands into barcodes. Barcode-assisted Raman analyses could enable on-site identification in nearly real-time, thus implementing preventive oral control, enabling prompt selection of the most effective drug, and increasing the probability to interrupt disease transmission.