Abstract: The increasing complexity of intelligent equipment and evolving operation and maintenance demands within Industry 4.0 highlight the inadequate adaptability of traditional maintenance decision-making methods in dynamic environments. Reinforcement learning (RL)-based maintence decision-making technology offers a paradigm for intelligent equipment maintenance by enabling autonomous strategy optimization through environmental interaction. This paper systematically explores the integration of RL theory and maintenance decision-making, focuses on 76 peer-reviewed articles published between 1954 and 2024. Core RL algorithms, including SARSA, Q-Learning, and Actor-Critic, are thoroughly examined and analyzed. The current state of intelligent equipment maintenance decision-making technology is also analyzed in depth. Typical application scenarios for RL in equipment maintenance decision-making are comprehensively dissected across four key areas: industrial manufacturing, energy, aerospace, and transportation. The study also identifies and discusses the core challenges facing current technology, such as algorithm convergence speed, computational efficiency, model interpretability, and issues related to data acquisition and privacy. This research provides a theoretical reference for algorithm innovation and engineering implementation in the field of intelligent operation and maintenance, fostering the deeper application of RL in maintenance decision-making.