Abstract: Traditional microbiological detection methods have inherent limitations in detection speed, sensitivity, and specificity, making them increasingly unable to meet growing clinical demands. In recent years, artificial intelligence (AI) has been rapidly integrated into clinical microbiological testing, with numerous studies demonstrating its significant potential to enhance pathogen identification, predict antimicrobial susceptibility testing, and advance laboratory automation. This article systematically reviews classical AI algorithms and their latest advancements in this field. For visual data applications, deep learning-based models are used to automatically analyze microscopy images or colony morphology, significantly improving recognition efficiency and diagnostic accuracy. For non-visual data, AI has achieved breakthroughs in analyzing multi-omics data such as genomics, transcriptomics, and metagenomics, and is widely used for rapid pathogen identification and prediction of antimicrobial resistance. Despite its promising prospects, the application of AI in clinical microbiological testing remains in the early stages of transitioning from scientific research to clinical practice. This paper further discusses the key challenges and opportunities encountered during this technological translation, aiming to help clinical professionals comprehensively understand the current status, future trends, and potential impact of AI in this field, thereby promoting its development into reliable and scalable routine diagnostic methods.