Offshore flowlines connect subsea wells, manifolds, and processing facilities, but any loss of containment can quickly escalate into environmental, operational, or reputational incidents. This review explores how smart leakage detection can mitigate environmental risk in Saudi Arabia’s offshore oil and gas flowlines by integrating process-based monitoring, distributed sensing, subsea robotics, machine learning, and environmental surveillance. A structured integrative review of recent peer-reviewed studies assessed leak initiation, signal discrimination, localization, environmental consequences, and integrity decision-making. Findings indicate that no single technology meets all requirements for early detection, low false-alarm rates, accurate localization, multiphase tolerance, and subsea maintainability. Internal methods are cost-effective and scalable but susceptible to instrumentation uncertainty and operational transients. External acoustic and fiber-optic methods enhance local sensitivity but depend on appropriate deployment and robust data interpretation. Machine learning improves discrimination when trained on representative field data, while digital twins link detection outputs to asset condition, consequences, and intervention strategies. For Saudi offshore environments, a layered, risk-based architecture is most effective: process analytics should provide broad surveillance, distributed or point sensing should protect critical segments, and mobile or satellite observations should verify uncertain events and support environmental response. The review recommends a confidence-rated decision pathway connecting detection to isolation, verification, environmental assessment, and integrity learning. Research priorities include validated multiphase datasets, small-leak field trials, uncertainty-aware fusion, cyber-resilient subsea sensing, and performance metrics focused on avoided releases rather than alarm accuracy alone.