Integrating Deep Learning-based Predictive Maintenance (DL-PM) in Industrial Internet of Things (IoT) applications optimizes operational efficiency and reduces downtime. This reaserch work explores the synergy between deep learning and predictive maintenance within the Industrial IoT context. The study traces the evolution from traditional maintenance to modern deep learning methods facilitated by IoT's real-time data acquisition. Methodologically, it details data preprocessing, model selection, and design for DL-PM. Convolutional Neural Networks (CNNs) analyze sensor data, Recurrent Neural Networks (RNNs) predict time-series patterns and hybrid models incorporate transfer learning. The reaserch work demonstrates DL-PM's application across industries through diverse case studies, evaluating its performance and comparing it to conventional approaches. It highlights challenges like data quality, model interpretability, scalability, ethical concerns, and biases. Future directions encompass advanced deep learning techniques, edge-cloud integration, collaborative learning, and strategies to overcome challenges. DL-PM's potential to revolutionize industrial processes in the IoT era is emphasized. This reaserch work underscores the transformative impact of Deep Learning-based Predictive Maintenance in Industrial IoT. It provides insights into implementation, challenges, and prospects, guiding industries towards efficient, downtime-minimized operations through DL-PM integration. © 2024 Elsevier B.V., All rights reserved.