The Tokamak Systems Monitor (TSM) is a software suite under development at ITER that provides operators with an integrated view of the tokamak’s engineering health based on operational instrumentation. A key functionality of TSM is anomaly detection, aimed at identifying unexpected behaviors across a wide range of systems. To this end, a dedicated anomaly detection module is being developed to integrate multiple machine learning-based algorithms, ranging from intershot classification of complete pulses to online detection of localized events. The current status of this module and its roadmap for future development are illustrated with two implemented examples: an intershot algorithm that uses dimensionality reduction and clustering to classify gyrotron pulses, and a time-localized approach based on an invertible neural network to monitor magnet power supplies. Automated warnings generated by the module will support operators in evaluating anomalies, thereby enhancing the reliability of ITER operations.