Surface-enhanced Raman scattering (SERS) is an inelastic scattering phenomenon that occurs when photons interact with substances, providing detailed molecular structure information. It exhibits various advantages including high sensitivity, specificity, and multiple-detection capabilities, which make it particularly effective in bacterial detection and antibiotic resistance research. In this review, we review the recent development of SERS-based approaches in the investigation of bacterial metabolism, antibiotic resistance, and species identification. Although the promising applications have been realized in clinical microbiology and diagnostics, several challenges still limit the further development, including signal variability, the complexity of spectral data interpretation, and the lack of standardized protocols. To overcome these obstacles, more reproducible and standardized methodologies, particularly in nanomaterial design and experimental condition optimization. Furthermore, the integration of SERS with machine learning and artificial intelligence can automate spectral analysis, improving the efficiency and accuracy of bacterial species identification, resistance marker detection, and metabolic monitoring. Combining SERS with other analytical techniques, such as mass spectrometry, fluorescence microscopy, or genomic sequencing, could provide a more comprehensive understanding of bacterial physiology and resistance mechanisms. As SERS technology advances, its applications are expected to extend beyond traditional microbiology to areas like environmental monitoring, food safety, and personalized medicine. In particular, the potential for SERS to be integrated into point-of-care diagnostic devices offers significant promise for enhancing diagnostics in resource-limited settings, providing cost-effective, rapid, and accessible solutions for bacterial infection and resistance detection.