Background Isolated rapid‐eye‐movement sleep behavior disorder (iRBD) is in most cases a prodrome of neurodegenerative synucleinopathies, affecting 1% to 2% of middle‐aged and older adults; however, accurate ambulatory diagnostic methods are not available. Questionnaires lack specificity in nonclinical populations. Wrist actigraphy can detect characteristic features in individuals with RBD; however, high‐frequency actigraphy has been rarely used. Objective The aim was to develop a machine learning classifier using high‐frequency (1‐second resolution) actigraphy and a short patient survey for detecting iRBD with high accuracy and precision. Methods The method involved analysis of home actigraphy data (for seven nights and more) and a nine‐item questionnaire (RBD Innsbruck inventory and three synucleinopathy prodromes of subjective hyposmia, constipation, and orthostatic dizziness) in a data set comprising 42 patients with iRBD, 21 sleep clinic patients with other sleep disorders, and 21 community controls. Results The actigraphy classifier achieved 95.2% (95% confidence interval CI: 88.3–98.7) sensitivity and 90.9% (95% CI: 82.1–95.8) precision. The questionnaire classifier achieved 90.6% accuracy and 92.7% precision, exceeding the performance of the Innsbruck RBD Inventory and prodromal questionnaire alone. Concordant predictions between actigraphy and questionnaire reached a specificity and precision of 100% (95% CI: 95.7–100.0) with 88.1% sensitivity (95% CI: 79.2–94.1) and outperformed any combination of actigraphy and a single question on RBD or prodromal symptoms. Conclusions Actigraphy detected iRBD with high accuracy in a mixed clinical and community cohort. This cost‐effective fully remote procedure can be used to diagnose iRBD in specialty outpatient settings and has potential for large‐scale screening of iRBD in the general population. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.