A Systematic Literature Review of Supervised Machine Learning Techniques for Predictive Maintenance in Industry 4.0

Industry 4.0 has driven a paradigm shift in manufacturing, pushing industries to adopt innovative technologies for more efficient decision-making. A key component of this revolution is predictive maintenance, which plays a central role in this transformation by leveraging, among others methods, supervised machine learning techniques to anticipate equipment failures, optimise maintenance schedules, and enhance operational efficiency. This study presents a Systematic Literature Review (SLR) of 216 peer-reviewed papers published between 2019 and 2024, analysing the adoption of supervised machine learning techniques for predictive maintenance in various industrial domains, including manufacturing, machinery, energy, and smart systems. Unlike previous SLRs that broadly examine ML applications, this review provides a structured taxonomy of predictive maintenance methods, highlighting their domain-specific usage and prevalence in safety-critical industries. Additionally, we analyse the types of datasets used, revealing a strong preference for real-world data but limited public availability, which poses challenges for reproducibility and benchmarking. This study identifies key trends in ML adoption and offers insights into future research directions, thereby reinforcing the need for open datasets, explainable AI, and cross-domain generalization.