Smart manufacturing and industrial Internet of things (IoT) have transformed the maintenance management concept from the conventional perspective of being reactive to being predictive. Recent advancements in this regard has resulted in development of effective prognostic health management (PHM) frameworks, which coupled with deep learning architectures have produced sophisticated techniques for remaining useful life (RUL) estimation. Accurately predicting the RUL significantly empowers the decision-making process and allows deployment of advanced maintenance strategies to improve the overall outcome in a timely fashion. In light of this, the paper proposes a novel noisy deep learning architecture consisting of multiple models designed in parallel, referred to as noisy and hybrid deep architecture for remaining useful life estimation (NBLSTM). The proposed NBLSTM is designed by integration of two parallel noisy deep architectures, i.e., a noisy convolutional neural network (CNN) to extract spatial features and a noisy bidirectional long short-term memory (BLSTM) to extract temporal information learning the dependencies of input data in both forward and backward directions. The two paths are connected through a fusion center consisting of fully connected multilayers, which combines their outputs and forms the target predicted RUL. To improve the robustness of the model, the NBLSTM is trained based on noisy input signals leading to significantly robust and enhanced generalization behavior. Through 100 Monte Carlo simulation runs performed under three different signal-to-noise ratio (SNR) values, it can be noted that utilization of the noisy training enhanced the results by reducing the standard deviation (std) between 9% and 67% across different settings in terms of the root-mean-square error (RMSE) and between 21% and 63% in terms of the score value. The proposed NBLSTM model is evaluated and tested based on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset provided by NASA, illustrating state-of-the-art results in comparison with its counterparts.