The integration of machine learning (ML) techniques in coastal engineering marks a paradigm shift in how coastal processes are modeled and understood. While traditional empirical and numerical models have been stalwarts in simulating coastal phenomena, the burgeoning complexity and computational demands have paved the way for data-driven approaches to take center stage. This review underscores the increasing preference for ML methods in coastal engineering, particularly in predictive tasks like wave pattern prediction, water level fluctuation, and morphology change. Although the scope of this review is not exhaustive, it aims to spotlight recent advancements and the capacity of ML techniques to harness vast datasets for more efficient and cost-effective simulations of coastal dynamics. However, challenges persist, including issues related to data availability and quality, algorithm selection, and model generalization. This entails addressing fundamental questions about data quantity and quality, determining optimal methodologies for specific problems, and refining techniques for model training and validation. The reviewed literature paints a promising picture of a future where ML not only complements but significantly enhances our ability to predict and manage the intricate dynamics of coastal environments.

Coastal flooding threatens lives, infrastructure, ecosystems, and economic activity, making reliable assessments essential for coastal planning and adaptation to sea-level rise. Coastal flood assessments over regional to global scales must reconcile broad spatial coverage with the local processes that control hazardous water levels and inundation. This review examines the physical foundations and methodological approaches used in large-scale assessments, with particular attention to the representation of wave contributions to total water level. Six complementary method groups are considered: analytical and empirical formulations, physics-based numerical models, physical experiments, data-driven models, GIS and remote sensing, and in situ observations. Selected large-scale studies are critically assessed in terms of wave parameterization, coastal morphology, interactions among water-level components, and validation. The review identifies recurring limitations associated with uncertain foreshore slopes, the transferability of empirical relationships, simplified process interactions, and scarce observations of overtopping and inundation. It also highlights that improved offshore forcing does not necessarily yield more reliable shoreline hazard estimates, and that total water level alone cannot determine inland flood extent. A modular, tiered framework combining regional screening with targeted high-resolution modelling and independent validation offers a practical means of balancing coverage and local detail. Further progress requires improved topographic and bathymetric data, systematic flood-event observations, and uncertainty propagation throughout the assessment chain.