Machine learning (ML) has increasingly been adopted in pipeline engineering, extending traditional physics-based, statistical, and empirical diagnostics toward data-driven frameworks. This review systematically examines ML applications in reliability-based design, structural integrity evaluation, condition monitoring, and inspection planning of pipelines. The literature shows a shift from conventional case-specific supervised learning toward transferable, hybrid, metaheuristic, and physics-informed techniques. These frameworks decompose signals, quantify uncertainty, use graph-based knowledge representation, and enhance generalizability through physics-informed learning, ranging from theory-guided features and architectures to limited forms of constraint enforcement. However, progress is limited by small, low-quality laboratory datasets, little focus on fatigue crack growth and multi-defect interactions, and heavy reliance on opaque surrogate models. A comparative analysis reveals persistent bottlenecks in generalization, interpretability, and the coupling of material, environmental, and operational variables. Three main research frontiers emerge: (1) large-scale, multi-source benchmark datasets, (2) physics-informed and interpretable learning frameworks that bridge domain mechanics and ML algorithms, and (3) standardized evaluation protocols and field-level validation schemes. The review concludes with a research roadmap to accelerate the move from algorithm-centric experimentation to tractable, trustworthy ML frameworks for pipeline integrity management. Overall, this study provides guidance for improving pipeline integrity to support safe and sustainable energy transport through resilient infrastructures.
Savari et al. (2026) studied this question.