(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
Predictive maintenance has become a cornerstone of smart manufacturing, transforming reactive and scheduled upkeep into data-driven, condition-based interventions that reduce downtime and extend asset lifetimes. However, the heterogeneity of industrial equipment, the diverse failure modes encountered across production lines, and the stringent real-time requirements of cyber-physical production systems render single-paradigm machine learning models insufficient for comprehensive health monitoring. This paper proposes a hybrid machine learning architecture that systematically integrates physics-informed models, deep representation learning, ensemble classifiers, and transfer learning mechanisms into a cohesive operational framework tailored for Industry 4.0 environments. The architecture is examined from a systems perspective, focusing on structural trade-offs among edge computing, fog layers, and cloud infrastructures, the governance of data pipelines, and the socio-technical implications of autonomous maintenance decisions. Emphasis is placed on the orchestration of hybrid models across the compute continuum, the assurance of data provenance and quality, and the policy frameworks needed to sustain fairness, interpretability, and human oversight in automated diagnosis and prognosis. By situating the hybrid architecture within broader discussions of infrastructure resilience, algorithmic transparency, and circular economy objectives, the paper articulates a forward-looking research agenda that positions predictive maintenance not merely as a technical optimization but as a critical component of responsible, sustainable, and equitable smart manufacturing ecosystems.