Water quality prediction is one of the important aspects of many water-related issues. Through water quality prediction, we can find signs of water quality deterioration, which facilitates decision-makers to take measures in advance. In this paper, a water quality prediction model based on genetic algorithm and SVM is used to adapt the weight of pollutants in current application to improve the accuracy of prediction on the basis of common water quality data. The model first uses the genetic algorithm to train the feature weight vector of the current data to adapt the weight to the current prediction, and then apply the feature weight vector in the SVM model training. After conducting experiments with a sewage treatment plant in Chongqing, the feasibility of the model in practical application was verified. Our study provides a new idea for water quality prediction.