In general, deep learning methods attained near human accuracy levels in multiple prediction and classification tasks such as text, video, speech, and image. Moreover, the network is generally provided with input, which is treated as black-box performance approximators to get the classified output. Further, the network incorporates the mission-critical process in the human–machine progressive function for medical planning, controlling, and diagnosis to perform machine output with associated trust. Generally, the output uncertainty is quantified using statistical metrics. However, the trust notion is mainly based on human visibility about the working of machines. Similarly, neural networks can provide human-understandable justification for their results leading to insights about the inner workings. The main objective of interpret-ability is to determine the human understanding level. Additionally, there is the chance for the interpretability to offer low network parameters regarding the input feature of the classified model. Hence, it is essential to overcome several limitations presented in the classical interpretability-based system. In the developed model, the Adaptive Deep Bayesian Learning (ADBL) mechanism is designed to enhance interpretability by identifying the bias. Enhancing the interpretability in the developed system enhances the accuracy robustness, and other performance in the developed system. The parameters in the developed ADBL model are tuned by the developed model named Hermit Crab Optimizer (HCO). At last, predictive maintenance data are used for analyzing the developed framework to improve the lifespan and also minimize the unplanned downtime of the system. Further, several experimental validations are performed in the developed framework to compute its effectualness over the classical techniques over different metrics.