Antifouling paints are used worldwide to coat and protect marine surfaces. However, when these coatings degrade or are cleaned away (e.g., by sandblasting), small particles can enter or be washed into marine ecosystems, where they eventually settle in the sediment due to their low density. These particles are considered a type of microplastic but have been little studied, partly due to the complexity of the work required to accurately identify and quantify them. The PaintSed project was designed to determine whether particles from antifouling paints (APPs) affect microbial communities in the surrounding sediment. Based on this information, the project’s goal was to use machine learning methods to create an APP prediction model based on microbial community data (16S amplicon sequencing). The project was successful: the model was tested and validated in 2025, and articles were published in the journals Microplastics and Nanoplastics, Science of the Total Environment, and Microbiology Spectrum.