Artificial intelligence (AI) has revolutionized food microbiological testing through significant technological advances. This review highlighted core innovations including deep learning-enhanced microscopic imaging， machine learning-integrated Raman spectroscopy， intelligent metagenomic analysis， and reinforcement learning-optimized culture systems. These AI-driven approaches demonstrated superior performance in processing speed， sensitivity， and throughput compared to conventional methods. Convolutional neural networks achieved minute-level colony enumeration efficiency， while Transformer models attained over 90% accuracy in antibiotic resistance gene prediction. However， challenges persist regarded data biases， algorithmic interpretability limitations， and generalizability across diverse food matrices. Current systems particularly required improved compatibility with regulatory frameworks. Future development necessitates deeper integration of industrial needed through multimodal data fusion algorithms and adaptive detection models. Establishing comprehensive microbial databases and interpretable AI architectures would facilitate standardized intelligent testing systems， enabling end-to-end food safety monitoring from production to consumption.