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As the main source of cooling in a city, water bodies play an important role in regulating the natural environment. However, there is a lack of analysis regarding the most effective methods for enhancing the cooling effect of different types of water bodies, as well as classifying the cooling effects based on parameters that affect the cooling effect. This hinders targeted optimization in urban construction. To address this gap, this study used remote-sensing data to develop a novel approach for classifying water bodies according to their actual cooling effect, and establishing a cooling effect prediction model for water bodies based on parameters that affect the cooling effect using machine learning methods. First, the cooling effect of 35 water samples from downtown Nanjing were classified by utilizing the data-mining method. Then the traditional method of classifying water bodies directly from parameters that affect the cooling effect and the classification prediction model established in this study were used to classify water bodies, respectively. It was found that the proposed classification prediction model constructed by combining unsupervised and supervised learning accurately and effectively determined the cooling effect classification of water bodies. Compared with the traditional classification method, the accuracy of the cooling effect classification by the novel approach can be improved by 1.46 times. Additionally, the study examined the correlation factors affecting the cooling effect of each class of water body and identified the most effective measures for improving the cooling effect of each class. It is noteworthy that for different classes, the most effective measures to exert the cooling effect are different. Starting from the parameters that affect the cooling effect of water bodies, this study offers targeted measures for respective water bodies to optimize their cooling density, range, and efficiency. This research provides a basis for using the cooling effect of water bodies more effectively and guiding urban planning and the construction of waterfronts. • A novel approach to examining the optimal use of the cooling effect of water bodies. • Water classification prediction model from physical properties to cooling effect. • Optimal parameters affecting cooling effect of each type of water body. • Guide the urban planning and construction of waterfront. [ABSTRACT FROM AUTHOR]