An improved KTBoost prediction model is proposed to address the low accuracy and poor fitting performance of the current KTBoost prediction model. First, the OGWO algorithm is put forward to solve the invalid iteration of the traditional gray wolf optimization (GWO) algorithm by using the arctangent function to optimize its convergence factor. Then, the OGWO algorithm is employed to optimize the hyperparameters in the KTBoost model, thereby improving the prediction accuracy of the model. Finally, the improved model and other prediction models are applied to traffic flow prediction scenarios for comparison to verify the feasibility of the model. The experimental results show that compared with the RBF model, random forest regression (RFR) model, KTBoost model, OGWO-RBF model, and OGWO-RFR model, the OGWO-KTBoost prediction model has better fitting performance and a better forecasting effect in practical application with its coefficient value of determination being 0.8265.