Abstract: With the development of social economy, various domestic water environment problems are gradually emerging. Water quality prediction based on large-sample environmental monitoring data plays a significant role in accurately formulating the countermeasure of environmental protection in advance, but there are fewer analytical studies related to the phasic summary of this subject. Based on the theory of bibliometrics, the article searches the papers in the field of water quality prediction included in the database of China Knowledge Network (CNKI) and WOS database from 2000 to 2023, and comprehensively overviews relevant domestic and foreign literature with VOSviewer software.By constructing a long time sequence mapping, the authors systematically comb the scientific research progress and achievements in the discipline, so as to exhibit the research status and trends of water quality prediction. The results show that water quality prediction research is a typical multi-author, multi-country, multi-institution cooperative field; China publishes the largest number of papers annually, and its scientific research outcomes has always been ranked in the top tier, indicating China's global leading role in the research of water quality prediction. By analysing the keywords, it is found that compared with traditional way, BP neural networks and deep learning are effective methods of water quality prediction in recent years. This study will be conducive to improving the domestic research of water quality prediction and provide bibliometric references for future research.