Yan Li ^1* Xiaoyan Cui ² Xiaoyan Yang ³ Guangqia Liu ⁴ Juan Zhang ^1*

• ¹ Department of Pharmacy, Jinan Fourth People's Hospital, Jinan, China
• ² Pharmacy Department，Jinan Huaiyin People's Hospital, Jinan, China
• ³ Pharmacy Department,Pingyin County Traditional Chinese Medicine Hospital, Jinan, China
• ⁴ Pharmacy Department, Jinan Licheng District Liubu Town Health Centre, Jinan, China

The issue of antimicrobial resistance (AMR) in pathogenic microorganisms has emerged as a global public health crisis, posing a significant threat to the modern healthcare system. The advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies has brought about revolutionary changes in this field. These advanced computational methods are capable of processing and analyzing large-scale biomedical data, thereby uncovering complex patterns and mechanisms behind the development of resistance. AI technologies are increasingly applied to predict the resistance of pathogens to various antibiotics based on gene content and genomic composition. This article reviews the latest advancements in AI and ML for predicting antimicrobial resistance in pathogenic microorganisms. We begin with an overview of the biological foundations of microbial resistance and its epidemiological research. Subsequently, we highlight the main AI and ML models used in resistance prediction, including but not limited to Support Vector Machines, Random Forests, and Deep Learning networks. Furthermore, we explore the major challenges in the field, such as data availability, model interpretability, and cross-species resistance prediction. Finally, we discuss new perspectives and solutions for research into microbial resistance through algorithm optimization, dataset expansion, and interdisciplinary collaboration. With the continuous advancement of AI technology, we will have the most powerful weapon in the fight against pathogenic microbial resistance in the future.