Manufactured sand refers to artificial sand whose particle size is less than 2.36 mm after the repeated crushing of gravels by sand-making machines. In experiments, stone powder and mud contents in the manufactured sand are called fine powder content, which represents the cleanliness of the manufactured sand. In this study, a method for predicting the fine powder content in the manufactured sand based on the XGBoost network is proposed. First, a completely closed image acquisition device is used to collect images of a solution made of fine powders in the manufactured sand, so as to guarantee that the outside light will not affect shooting. Then pre-treatment is carried out, such as picture cropping, RGB value reading, and LCH color space shifting, and an XGBoost network model is built. Through the Bayes principle, loop iteration of parameters is conducted, and the model is optimized, so as to make the r2_score of the model higher and finally predict the fine powder content in the manufactured sand. The results show that the r2_score of the data predicted by this model can reach 0.967 762. In addition, the r2_score predicted by the traditional multiple linear regression models, BP neural network, and traditional XGBoost network is 0.896 144, 0.914 598, and 0.950 670. In contrast, the prediction accuracy of the proposed model is significantly improved. In practical application, this method can shorten the measurement time and simplify the measurement steps of the fine powder content in the manufactured sand. Therefore, it is a new method for predicting the fine powder content in manufactured sand.