This chapter provides a synthesis of the concepts, methods, and findings presented throughout this monograph on data-driven sea state estimation (SSE) from ship motion data. The central premise has been to treat operational vessels as opportunistic wave buoys and to harness advances in deep learning and time-series modeling to infer sea state descriptors directly from multivariate motion responses. By systematically developing architectures and learning strategies that address key challenges such as nonlinear dynamics, class imbalance, cross-vessel variability, and robustness, the book has sought to establish a coherent methodological foundation for machine learning based SSE in modern maritime operations.