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Precise detection of recurrence in patients with treated nasopharyngeal carcinoma (NPC) facilitates timely intervention and prolongs survival. However, there is no compelling tool realizing real-time precise recurrence detection as scale hitherto. Here we present a deep learning-based sequential scan model called RAIN, harnessing 10,212 time-series follow-up head and neck magnetic resonance (MR) scans of 1,808 patients with treated NPC in a multicenter observational study (Blinded ID). The RAIN yields larger area under the receiver operating curve (AUC) values than single scan model (internal: 0.916 vs 0.855, p = 0.004; external: 0.900 vs 0.709, p