Rapid development of the Industrial Internet of Things (IIoT) is turning the management of machinery and decision making into a multitude of operational data. A key issue in such an environment would be the ability to predict the failure of assets well to reduce downtimes and maximize on performance. The role of Predictive Maintenance (PdM) is significant here, although the current literature usually focuses on the development of the algorithms and ignores the consideration of technological, organizational, and industrial aspects. The paper will provide an extensive overview of AI-driven PdM in the framework of Industry 4.0 and suggest a hybrid taxonomy that will combine AI paradigms, stages of maintenance lifecycle, and industrial deployment scenarios. The taxonomy offers a multidimensional viewpoint connecting data analytics, machine learning and operational readiness as a conceptual framework to tie together scholarly research and industrial practice. The paper critically examines the existing issues such as data heterogeneity, model interpretability, and scalability and presents research gaps that impede the adoption of PdM, in particular by small and medium-sized enterprises (SMEs). This work provides a systematic basis to the development of explainable, adaptive, and interoperable PdM systems by providing engineering and computer science viewpoints. The results highlight the necessity of multidisciplinary solutions that can make the AI innovation stay relevant to the real-world maintenance plans and create resilient and smart industrial ecosystems. © 2014 IEEE.