Adverse drug reactions (ADRs) significantly impact clinical medication safety. The timely identification and prediction of ADRs rely on the efficient analysis of real-world data, such as electronic health records, social media, and spontaneous reporting databases. In recent years, the rapid advancement of artificial intelligence, particularly large language models, in natural language processing, causal reasoning, and complex data mining has provided new technological means for real-time ADRs monitoring and individualized prediction. This paper summarizes the latest research achievements in AI-driven ADRs monitoring. Focusing on diverse data sources, including structured databases and electronic health records, it elaborates on the advantages andchallenges of AI in ADRs event extraction, relationship identification, causal analysis, and risk prediction. The aim is to provide a theoretical reference for constructing more intelligent and efficient ADRs monitoring systems.