Abstract: The problem of eutrophication of water bodies is becoming increasingly serious,a phenomenon that leads to an abnormal growth of phytoplankton and the release of harmful algal toxins,which contribute to the pollution of water bodies and pose a risk to flora, fauna and even humans. Related to this, water bodies are often slow-moving, poorly connected and have long stagnation times, making them more vulnerable to eutrophication and increasing the risk of blooms. In this paper, the impacts of hydrodynamic factors, water environment factors and meteorological factors are discussed in depth. Hydrodynamic factors include water velocity, flow rate and water disturbance, which together determine the degree of algae accumulation; water environment factors include water temperature, dissolved oxygen, nutrient salt concentration and pH, which directly affect the growth of planktonic algae; meteorological factors such as air temperature and wind will change the temperature of the water body, which will have an impact on the formation and development of the bloom. This paper also discusses the application of numerical model and data-driven model in the prediction of water bloom based on the characteristics of water bodies of rivers, lakes and reservoirs. Numerical model is used in the prediction of water bloom to predict the occurrence and development trend of water bloom by simulating the environmental factors in the water body; the data-driven model is based on data mining algorithms and statistical techniques, and uses the machine learning method to iteratively learn the correspondence relationship between the monitoring data and pictures and the bloom by analysing and identifying the monitoring data and pictures. correspondence, and iterative learning to create their predictive links with the outbreak of waterwaters. They provide an effective tool for exploring the condition of water bodies and predicting water bloom events, and their coupled approach is of great significance for the accurate prediction of water bloom, which is also the development trend of future research, and will help water resource managers to better regulate the water body, and improve the prediction accuracy and the efficiency of water quality management in order to reduce the occurrence of water bloom.