With antimicrobial drugs widely used in the clinic, the drug resistance of pathogenic bacteria is becoming more and more serious. Rapid, highly sensitive, and accurate detection of bacteria and their drug susceptibility is the key to alleviating bacterial resistance. Surface-Enhanced Raman Scattering (SERS), can be used to obtain molecular fingerprint information directly and has become an effective detection technology for bacteria and their drug resistance. The molecular composition and structure of different species of bacteria are different, and the characteristic Raman signal of bacteria will also change before and after antibiotic treatment, which provides the basis for the application of SERS for the detection of pathogenic bacteria and their drug resistance. Based on the differences in molecular composition and structure, combined with traditional multi-class data analysis and machine learning algorithms, SERS can provide objective diagnostic information. In this review, the research progress of SERS for the detection of pathogenic bacteria and their drug resistance in recent years is reviewed, and the current problems existing in the application of SERS in the detection of pathogenic bacteria are also described. Firstly, the materials and structures of SERS substrates commonly used to detect pathogenic bacteria and their drug resistance were discussed, including gold nanoparticles, silver nanoparticles, silver-coated gold nanoparticles and composite SERS substrates formed by the combination of new nanomaterials and nanoparticles. Then, the methods of capturing bacteria in SERS detection were summarized, mainly based on nucleic acid aptamers, immunomagnetic separation, microfluidic systems and electrostatic binding. The principles and methods of the above methods were described, and the research progress of the above methods was summarized. Finally, various data analysis methods of SERS spectra of pathogenic bacteria were summarized. Through spectral preprocessing, feature extraction, classification and recognition, and the establishment of a diagnosis model of pathogenic bacteria, the detection of pathogenic bacteria and their drug resistance was realized. The traditional data analysis methods and machine learning methods were compared, and the advantages and applications of deep learning algorithms in the SERS detection of pathogenic bacteria and their drug resistance were introduced. In this paper, the key issues in the application of SERS in the detection of pathogenic bacteria and their drug resistance were also discussed, and the detection methods of pathogenic bacteria and their drug resistance based on SERS prospected, to promote the application of surface-enhanced Raman spectroscopy in clinical detection.