Prescriptive Maintenance (PsM) transforms industrial asset management by enabling autonomous decisions through simultaneous failure anticipation and optimal maintenance recommendations. Yet, despite increasing research interest, the conceptual clarity, technological maturity, and practical deployment of PsM remains fragmented. Here, we conduct a comprehensive and application-oriented Systematic Literature Review of studies published between 2013–2024. We identify key enablers—artificial intelligence and machine learning, horizontal and vertical integration, and deep reinforcement learning—that map the functional space of PsM across industrial sectors. The results from our multivariate meta-synthesis uncover three main thematic research clusters, ranging from decision-automation of technical (multi)component-level systems to strategic and organisational-support strategies. Notably, while predictive models are widely adopted, the translation of these capabilities to PsM remains limited. Primary reasons include semantic interoperability, real-time optimisation, and deployment scalability. As a response, a structured research agenda is proposed to emphasise hybrid architectures, context-aware prescription mechanisms, and alignment with Industry 5.0 principles of human-centricity, resilience, and sustainability. The review establishes a critical foundation for future advances in intelligent, explainable, and action-oriented maintenance systems.