Reliable forecasting of ocean conditions is essential to safe, efficient offshore operations—and new technologies are redefining what’s possible. This session showcases advances in metocean observation, modeling, and prediction, spanning direct measurement–model comparisons from deepwater deployments to machine learning–based forecasts of Loop Current behavior. Papers explore probabilistic diffusion modeling, data assimilation using Argo and autonomous surveys, and emerging operational systems for the Gulf of Mexico and tropical Atlantic. Together, they highlight how AI, advanced empirical methods, and integrated data frameworks are transforming model skill, risk prediction, and operational planning for offshore energy systems in a changing ocean