The global rise of antimicrobial resistance (AMR) presents a critical challenge to public health, necessitating the development of advanced diagnostic tools for rapid and accurate detection. Imaging technologies facilitate examination of biological processes at molecular and cellular levels, explanation of resistance mechanisms, and monitoring resistant strains. The integration of artificial intelligence (AI) with surface-enhanced Raman spectroscopy (SERS) imaging has emerged as an innovative approach to tackle this challenge. SERS imaging offers high sensitivity and molecular specificity, enabling the identification of resistant bacterial strains and their mechanisms at a single-cell level. AI-driven data analysis further enhances diagnostic potential by automating pattern identification, revealing minor spectrum properties, and improving the speed and accuracy of AMR detection. This combination allows for real-time, label-free Raman imaging of microbial populations, providing insights into resistance mechanisms, and the interactions between pathogens and antimicrobial agents. By combining the complementary capabilities of AI and SERS imaging, this unique approach offers reliable AMR diagnosis and facilitates the development of treatment solutions to address the global AMR crisis.