Modeling the turbulent flow within different draft tube configurations in a cost-effective way is essential for efficient turbine optimization and exploring the underlying flow mechanics. In this study, a convolution neural network (CNN) based surrogate model was proposed to predict local flow parameters within different inclined Francis turbine draft tubes. Three symbolic representations denoting the complex geometry and boundary conditions were set as the input, and pressure and velocity were the output. The adopted CNN framework consists of the U-Net architecture with a contracting path and four expansive paths. Six representative hyperparameters were considered to analyze their influence on the performance and generalization ability of the CNN model. The results show that the predicting accuracy of the CNN model with a U-Net network is 7.53% higher than the traditional CNN model, as skip connections improve image segmentation accuracy. The CNN model with a larger convolution kernel can more comprehensively capture the main features of the flow field. The model with three input variables improves prediction accuracy by 2.4% as more geometrical features correlate with the key flow patterns. For the four different image resolutions, the model with a resolution of 200 × 400 performs exceptionally well. In addition, appropriately increasing the number of convolutional layers or blocks can significantly improve the prediction accuracy of the CNN model. The proposed innovative surrogate model is useful for facilitating the optimization of hydraulic turbine components.