Remaining useful life (RUL) prediction for condition-based maintenance decision making plays a key role in prognostics and health management (PHM). Accurately predicting RUL of the rotating components of complex machines becomes a challenging task for PHM. For many existing methods, the current prediction error of RUL prediction may be accumulated into the future predictions, and thus can lead to a prediction error superposition problem. In this article, the formation mechanism of prediction error superposition is analyzed, and for the first time a deep adversarial long short-term memory (LSTM) prognostic framework is proposed to overcome the major issue related to prediction error superposition. In the proposed framework, a generative adversarial network (GAN) architecture combining the LSTM network and autoencoder (AE) is investigated for bearing RUL monitoring. In the proposed deep adversarial learning prediction framework, due to the potential involvement of long-term and complex tasks, the LSTM network (generator) is used to predict the degradation process of rolling bearings based on available historical data, and a simple but useful AE (discriminator) is used to determine and refine the accuracy of the prediction. Therefore, the AE plays the adversarial role of the LSTM network, and the prediction accuracy of the LSTM network can be significantly improved. For illustration purpose, two practical case studies, which use a series of bearing degradation data and the IEEE PHM 2012 PRONOSTIA datasets, respectively, are presented to show the prediction performance of the proposed method. Experimental results show that the proposed method works very well for vibration monitoring and performs better in comparison with the reference machine learning and deep learning approaches. Impact Statement—The damage of rolling bearing usually leads to a significant consequence to industrial production process. However, the existing remaining useful life (RUL) prediction methods for rolling bearing have a prediction error superposition problem that can affect the multistep prediction performance. The new adversarial learning prognostics model proposed in this article can overcome the problem. The proposed method uses LSTM network as a generator to predict RUL life for rolling bearing, and uses AE as a discriminator to estimate the prediction accuracy. The method can significantly improve the multistep prediction accuracy of RUL for rolling bearing, and provides reliable and scientific strategy in PHM of mechatronics equipment.