Cervical cancer is the fourth most frequent cancer in women worldwide and the third in Brazil. Screening methods can substantially reduce new cases of cervical cancer by identifying pre-cancerous lesions, making it possible to offer correct management and treatment. For this purpose, this work reports the use of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with principal component analysis (PCA) and variable selection techniques, such as successive projections algorithm (SPA) and genetic algorithm (GA) associated to linear or quadratic discriminant analysis (LDA/QDA), to classify samples for negative for intraepithelial lesion or malignancy (NILM), n = 43, and squamous intraepithelial lesion (SIL), n = 40, directly from blood plasma. Furthermore, the possibility to categorize SIL subclasses according to low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL) lesion degrees was evaluated. Application of variable selection algorithms, especially GA, considerably improved the classifications by choosing spectral variables that reflect the chemical differences between a healthy and pre-cancerous plasma sample. This method was able to correctly classify NILM vs. SIL with sensitivity and specificity for both classes varying around 77% using LDA. With QDA, the results were enhanced to sensitivity around 90% and specificity of 83%. NILM vs. LSIL presented sensitivity and specificity ranging between 67–94% and 82–94%, respectively. In addition, NILM vs. HSIL were found to have sensitivity and specificity from 76–97% to 73–100%, respectively, where QDA substantially provided better classifications. These findings highlight the potentiality of ATR-FTIR spectroscopy combined with multivariate analysis as a screening tool for pre-cancerous cervical lesions, which could contribute to reduce cervical cancer incidence.