In order to study how to accurately predict the development trend of track irregularity in the absence of historical data. A track irregularity prediction method that can consider the prior information of experts is proposed. The questionnaire survey method is used to obtain expert experience information and build a Bayesian linear regression model with prior information. Then the Markov chain Monte Carlo method is used to solve the model parameters. Finally, the amplitude of track irregularity is predicted and error analysis is conducted, and the prediction effects of different models in the absence of historical data are compared. The results show that the method can accurately predict the development trend of the track irregularity in the short term, and the correlation coefficients are all above 0.9. In the absence of historical data, the Bayesian linear regression model can also maintain a high prediction accuracy and the R2 is 0.88, which is 17% higher than the traditional linear regression model. It is concluded that this method can effectively improve the accuracy of the operation and maintenance plan formulation at the initial stage of line operation, and is helpful to the realization of preventive maintenance of high-speed railway lines.