The ocean's biological carbon pump plays a major role in climate and biogeochemical cycles. Photosynthesis at the surface produces particles that are exported to the deep ocean by gravity. Sediment traps, which measure deep-carbon fluxes, help to quantify the carbon stored by this process. However, it is challenging to precisely identify the surface origin of particles trapped thousands of meters deep due to the influence of ocean circulation on the sinking path of carbon. In this study, we conducted a series of numerical Lagrangian experiments in the Porcupine Abyssal Plain region of the North Atlantic and developed a machine learning approach to predict the surface origin of particles trapped in a deep-ocean sediment trap. Our numerical experiments support the predictive performance of the machine learning approach, and surface conditions appear to provide valuable information for accurately predicting the source area, suggesting a potential application with satellite data. We also identify factors that potentially affect prediction efficiency, and we show that the best predictions are associated with low kinetic energy and the presence of mesoscale eddies above the trap. This new tool could provide a better link between satellite-derived sea surface observations and deep-ocean sediment trap measurements, ultimately improving our understanding of the biological-carbon-pump mechanism.