Cachaça, an alcoholic beverage derived from the fermentation of sugarcane juice, is a quintessential Brazilian product, with the Brejo Paraibano region standing out as one of the leading producers of high-quality cachaças. Therefore, geographic authentication of these beverages is essential to guarantee their quality and prevent fraud. This study employed mid-infrared spectroscopy (MIR) combined with chemometric models, including One-Class Partial Least Squares (OC-PLS) and Data-Driven Soft Independent Modeling by Class Analogy (DD-SIMCA), to authenticate the geographic origin of cachaças from Brejo Paraibano, as opposed to cachaças originating from other Brazilian regions. The DD-SIMCA model, incorporating spectral preprocessing with baseline shift correction and Savitzky-Golay smoothing using 21 points, achieved 95.6 % sensitivity during training, 100 % in testing, and 100 % specificity, with an overall classification efficiency of 98.4 %. These findings underscore the effectiveness of the proposed approach as a robust, green, and reliable tool to verify the authenticity and geographic origin of cachaças.