When using direct ink writing technology to prepare FGMs (functionally graded materials) parts, there is a delay in the change of material ratio, which leads to poor consistency between the material ratio of the prepared parts and the design goals, and increases the uncertainty of material properties, resulting in potential use risks. In order to accurately learn the delay information of material ratio under different process parameters, a neural network prediction model based on computational fluid dynamics was constructed. Based on RNG k-\epsilon model, an optimized Bayesian regularized neural network model was used to predict the delay information corresponding to different process parameters, namely, the delivery delay time and global delay time were predicted under different initial ratio and target ratio of materials, screw rotation speed, sum of double extruded plunger feed rates. The prediction accuracy could reach 94.87% and 92.74%, respectively. Using digital image processing methods to process FGMs samples printed under different process parameters, the results showed that the material gradient change curve of the actual printed samples had a high degree of coincidence with the simulation results, verifying the accuracy of the simulation results based on computational fluid dynamics as the analysis framework, as well as the feasibility and reliability of the constructed optimized neural network model for predicting delay information. The research results provide a reference for integrating digital prediction methods into FGMs part body preparation processes in the future, and can promote the transformation of traditional manufacturing models to digital manufacturing models, ultimately achieving the accurate manufacturing of FGMs parts.