The application fields of piezoresistive pressure sensor in recent years have become more and more extensive. Besides, the high reliability of the sensor is also required. However, considering some sensors operate in hostile environments and need to ensure continuous operation accuracy, Prognostics and Health Management (PHM) for piezoresistive pressure sensor should not be ignored. To solve this problem, a fault diagnosis and prognostic method, which combines with Support Vector Machine (SVM) and deep gated recurrent unit network (DGRU) optimized by Hunter-prey Optimization (HPO), is proposed in this paper. First, three fault types of the sensor are defined. Second, SVM is adopted to realize the fault diagnosis. Third, two layers DGRU is employed to predict the health index which is defined to represent the health state of the sensor. Meanwhile, the optimal parameters of the DGRU are optimized by HPO algorithms. Finally, the remaining useful life (RUL) can be estimated by the predicted health index and failure threshold. The proposed method in this paper is proved to be effective and accurate. The fault diagnosis accuracy is 100% in the three fault types defined by this paper. The minimum mean absolute error is 6 ×10-5. It proves the proposed method of PHM in this paper is applicable in real application.