As an important component of the hydraulic system, check valve affects the work efficiency of the hydraulic system and the safe and stable operation of the equipment when internal leakage occurs. Aiming at the leakage prediction in check valve, a nondestructive leakage prediction method in check valve based on multi-source signal and cuckoo search support vector regression ( CSSVR) is proposed. Firstly, a leakage detection experimental platform in the check valve is established, and the vibration signal and acoustic emission signal leaking in the check valve under the different working conditions and the different fault characteristics are obtained on the platform. Secondly, wavelet packet energy analysis method is used to extract the root mean square (RMS) of optimal frequency band reconstruction signals which are as the predictors with the inlet pressure to establish a multi-source signal prediction model based in CS-SVR. Finally, with compared and analyzed the models, the results show that the cuckoo search (CS) has a greater advantage over the grid search (GS) and particle swarm optimization (PSO) for SVR model parameters. Prediction methods based on multi-source signal inputs are more accurate than single-source input prediction methods, and the proposed method can realize the prediction of the internal leakage rate of the check valve with different pressure, different fault categories and different fault degrees. The proposed method average relative error is 8. 97% and has high robustness, which lays a foundation for the development of non-destructive testing application technology for valve leakage.