ABSTRACT: Aging big-diameter underground steel pipelines pose significant sustainability and operational challenges for bulk water distribution utilities. These challenges include, but are not limited to, an increased risk of underground big-diameter pipeline failure and rising costs for asset condition assessments. We developed and tested a unsupervised machine-learning framework to improve pipeline condition assessment, predictive maintenance, and inspection prioritisation using real-world secondary data. We combined mixed-data clustering, non-linear dimensionality reduction, anomaly detection, and association rule mining to identify complex patterns in the condition of underground steel pipelines without excavation. Our results indicate that mixed-type clustering methods produce stable, well-separated condition groups and outperform numeric-only methods. Non-linear embeddings show clear separability, and anomaly detection reliably pinpoints high-risk pipeline segments. Association rules reveal hidden connections between pipeline attributes, enhancing clarity and engineering relevance. This framework enables data-driven decision-making, reduces unplanned maintenance, and supports efficient resource use by extending the lifespans of large-diameter underground pipeline assets and boosting operational reliability. This study supports sustainable asset management and improved operations in bulk water distribution pipeline systems through practical, scalable unsupervised analytics. Keywords: Unsupervised Machine Learning, Big-diameter steel pipeline, Water Utilities.