Ocean ecosystems are undergoing rapid transformation that is driving redistribution of highly mobile marine species. Anticipating these changes is critical for sustainable fisheries management and effective marine spatial planning, yet many existing species distribution model frameworks are not designed specifically for near-term forecasting and decision support. Here, we integrate satellite remote sensing, high-resolution oceanographic model outputs, fisheries observer data, and biologging observations from highly mobile predators (e.g., sharks and tunas) within a spatially explicit AI/ML model framework to generate short-term forecasts of species' distributions in the Northwest Atlantic Ocean. We implement an interpretable AI approach and use transparent evaluation metrics to identify key environmental drivers and improve confidence in operational forecast tools. By comparing model predictions to observed target and incidental catch, we evaluate forecasts in a year-ahead hindcast framework and develop decision-relevant metrics that quantify both target species encounter probability and bycatch risk, including the unrealized benefits of using near-term forecast tools for decision making on the water. By combining model predictions across target and non-target species, we generate dynamic maps of fishing opportunity and ecological risk to inform adaptive management. Our results demonstrate how interpretable forecasting models can predict spatiotemporal overlap between fisheries and vulnerable species and support dynamic spatial measures by empowering fisher decision-making and/or supporting time-area management mechanisms. This work highlights the potential for interpretable AI to advance proactive and transparent decision support for spatial planning and resource use applications in dynamic ocean systems.