Smart electrical distribution networks are becoming more observable, automated, and data intensive, yet maintenance practice often remains dominated by periodic inspection or intervention after alarms and failures. This review develops a predictive maintenance framework that connects artificial intelligence and data analytics with the operational realities of distribution transformers, on-load tap changers, switchgear, circuit breakers, feeders, cables, substations, smart meters, and distributed-energy interfaces. The evidence base is restricted to peer-reviewed work published from 2020 to 2025 and is synthesized through a structured integrative review focused on data provenance, condition indicators, learning tasks, validation practice, interpretability, uncertainty, deployment constraints, and maintenance decision linkage. The review shows that useful predictive maintenance cannot be reduced to selecting a high-performing classifier. Reliable deployment requires a chain of asset-aware sensing, data-quality control, representation learning, anomaly detection, fault diagnosis, health forecasting, uncertainty estimation, consequence assessment, and human-governed maintenance orchestration. Deep learning and ensemble methods improve representation of nonlinear and temporal degradation, while digital twins can provide synchronized context, scenario testing, and traceability; however, scarce fault labels, changing operating regimes, class imbalance, fragmented semantics, cyber risk, and weak external validation remain major obstacles. A layered framework is therefore proposed in which analytical outputs are converted into risk-calibrated maintenance actions rather than isolated predictions. The framework emphasizes evidence quality, cross-asset generalization, explainable reasoning, model monitoring, and feedback from completed work orders. It provides a research and implementation architecture for moving smart distribution maintenance from condition visibility toward dependable, auditable, and economically meaningful predictive decision support.