<p>In the design of lightweight and extended-span structures, cable-based systems have been widely used and may be the only solution, particularly for superlong-span bridges and spatial structures. Prestressed anchor cables are the most crucial element in cable-based structures and directly influence their performance. However, prestressed anchor cables are prone to corrosion and fatigue due to environmental factors, complex loads, and chemical influences. Therefore, to ensure safety, developing a real-time monitoring method to evaluate the status of the prestressed cable is urgently needed. This paper introduces Acoustic Emission (AE) integrated with machine learning to enhance the diagnosis and prognosis of prestressed anchor cables. In the laboratory, twelve cables with varying defects were tested to failure to optimize the machine learning framework. Meanwhile, AE signatures due to damage progression were characterized and identified. Moreover, a machine learning framework for prestressed anchor cables was developed in terms of k-means and k-Nearest Neighbor (K-NN) clustering to distinguish AE signatures precisely due to different AE sources from large-scale data. A precursor signal for cable fracture was also extracted and recommended for early-stage warning. Furthermore, the in-situ recorded AE signal has validated the proposed framework. This study provides guidelines for using AE as a valuable tool for the Structural Health Monitoring (SHM) of prestressed anchor cables.</p>