© 2022 Elsevier B.V.Colistin-resistant Klebsiella pneumoniae (ColR-Kp) causes high mortality rates since colistin is used as the last-line antibiotic against multi-drug resistant Gram-negative bacteria. To reduce infections and mortality rates caused by ColR-Kp fast and reliable detection techniques are vital. In this study, we used a label-free surface-enhanced Raman scattering (SERS)-based sensor with machine learning algorithms to discriminate colistin-resistant and susceptible strains of K. pneumoniae. A total of 16 K. pneumoniae strains were incubated in tryptic soy broth (TSB) for 4 h. Collected SERS spectra of ColR-Kp and colistin susceptible K. pneumoniae (ColS-Kp) have shown some spectral differences that hard to discriminate by the naked eye. To extract discriminative features from the dataset, autoencoder and principal component analysis (PCA) that extract features in a non-linear and linear manner, respectively were performed. Extracted features were fed into the support vector machine (SVM) classifier to discriminate K. pneumoniae strains. Classifier performance was evaluated by using features extracted by each feature extraction techniques. Classification results of SVM classifier with extracted features by an autoencoder (autoencoder-SVM) has shown better performance than SVM classifier with extracted features by PCA (PCA-SVM). The accuracy, sensitivity, specificity, and area under curve (AUC) value of the autoencoder-SVM model were found as 94%, 94.2%, 93.8%, and 0.98, respectively. Furthermore, the autoencoder-SVM model has demonstrated statistically significantly better classifier performance than PCA-SVM in terms of accuracy and AUC values. These results illustrate that non-linear features can be more discriminative than linear ones to determine SERS spectral data of antibiotic-resistant and susceptible bacteria. Our methodological approach enables rapid and high accuracy detection of ColR-Kp and ColS-Kp, suggesting that this can be a promising tool to limit colistin resistance.