The last years, non-targeted fingerprinting by Fourier-transform infrared (FT-IR) spectroscopy has gained popularity as an alternative to classical gas chromatography (GC)-based methods because it may allow fast, green, non-destructive and cost-effective assessment of quality of essential oils (EOs) from single plant species. As the relevant studies for Laurus nobilis L. (bay laurel) EO are limited, the present one aimed at exploring the diagnostic potential of FT-IR fingerprinting for the identification of its botanical integrity. A reference spectroscopic dataset of 97 bay laurel EOs containing meaningful information about the intra-species variation was developed via principal component analysis (PCA). This dataset was used to train a one-class model via soft independent modelling class analogy (SIMCA). The model was challenged against commercial bay laurel and non-bay laurel EOs of non-traceable production history. Overall, the diagnostic importance of spectral bands at 3060, 1380–1360, 1150 and 1138 cm−1 was assessed using GC-FID-MS data. The findings support the introduction of FT-IR as a green analytical technique in the quality control of these often mislabeled and/or adulterated precious products. Continuous evaluation of the model performance against newly acquired authentic EOs from all producing regions is needed to ensure validity over time.