Following the launch of the EarthCARE (Earth Clouds, Aerosols and Radiation Explorer) satellite in May 2024, the Multi-spectral Imager (MSI) supplements the vertical information retrieved from the Atmospheric Lidar (ATLID) and the Cloud Profiling Radar (CPR) instruments by providing information on cloud and aerosol properties in the across-track direction. MSI features a 150 km swath and a spatial resolution of 500 m across its four solar and three thermal channels. The operational cloud mask algorithm does not distinguish between clouds and aerosols, potentially leading to misclassifications of5 thick aerosol layers over ocean originating from dust storms, wildfires, or volcanic eruptions as clouds. Existing dust detection algorithms rely on spectral channels that are not available on MSI. This study therefore introduces a dust detection algorithm over ocean for MSI based on a random forest (RF) classification model that operates with the reduced spectral information available from the instrument. RF is a machine-learning approach that constructs multiple decision trees, whose outcomes are aggregated to generate accurate and reliable predictions. Pre-launch of the EarthCARE mission, a precursor dust detection10 algorithm was developed with Moderate-resolution Imaging Spectroradiometer (MODIS) data, using channels similar to those of MSI and adjusted to match MSI’s swath dimensions. Two case studies of dust storm outbreaks captured by MSI demonstrate the model’s capability and highlight its potential for operational use.