Predictive maintenance (PdM), a widely used approach for improving reliability, safety, and cost-efficiency in industrial operations, relies on advanced analytics and data-driven insights to detect incipient failures and optimize system performance. While conventional machine learning (ML) techniques are widely applied in PdM, they often struggle with noisy, high-dimensional sensor data. Recent progress in quantum machine learning (QML) offers potentially useful directions for improving fault diagnosis and predictive accuracy. This motivates a systematic literature review that assesses QML applications in PdM, examines technical and practical challenges shaping their development, and identifies opportunities for future research and industrial adoption. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we screened 238 records and finalized 18 empirical studies published between 2015 and 2025 across five major digital libraries. Our synthesis indicates that QML-for-PdM studies primarily use quantum kernel methods and shallow variational hybrid models, typically employing angle-based encoding, controlled-Z (CZ) entanglement, and circuit depth . Reported results suggest performance advantages of QML over classical baselines in curated settings, though the supporting evidence remains concentrated in highly controlled, few-qubit experimental environments. However, reported performance degrades under positive–unlabeled noise, over-parameterization, and unmitigated hardware noise. Overall, this review provides an evidence-based assessment of QML-based predictive maintenance by synthesizing reported benefits, practical constraints, and priority directions for future work.