Compound flooding (CoMF) has emerged as a critical natural hazard for coastal and estuarine regions, where interacting fluvial, pluvial, groundwater, tidal, and storm surge processes generate non-linear amplification effects that challenge conventional single driver flood risk assessments. Yet, despite major advances in compound flood science, integrated literature reviews that link multivariate hazard dynamics, modelling approaches, risk management, and climate adaptation strategies remain limited. This systematic review synthesises three decades of research on CoMF in Cork Harbour and Catchment (CHC), a highly dynamic coastal-estuarine system increasingly exposed to climate change. Following PRISMA guidelines, 503 documents were screened across Web of Science, Scopus, Google Scholar, and grey literature sources, identifying 59 peer-reviewed articles and 28 technical reports and policy documents published between 1991 and 2025.</p> <p>This review provides two key contributions of international importance. First, it positions CHC as a potential national reference site and globally relevant compound flood model system, demonstrating how climate driven interactions between extreme rainfall, river discharge, storm surge, tides, and Sea-Level Rise (SLR) generate non-linear amplification effects. Results reveal that while fluvial processes currently dominate with a 30 % increase in future flood inundation, projected SLR and surge intensification could amplify coastal inundation by up to 400 %, surpassing fluvial contributions and shifting the dominant contribution towards coastal mechanisms. Second, it offers a transferable methodological scheme, showcasing best practice integration of multi-scale hydrodynamic modelling, multivariate statistical dependence analysis, and emerging physics informed AI tools capable of improving real time forecasting. Advances in high-resolution hydrodynamic modelling and multivariate statistical frameworks have improved simulation fidelity, yet critical gaps persist—particularly in probabilistic modelling, artificial intelligence (AI) enhanced real-time forecasting, and integration of urban drainage and socio-ecological resilience.</p> <p>We identified six priority future research directions that could assist in implementing local action and hence have transferable international relevance: (i) probabilistic and machine-learning approaches for compound flood hazard prediction; (ii) integrated modelling frameworks coupling climatic, hydrodynamic, and socio-ecological systems; (iii) continuous climate and hydrological monitoring infrastructure; (iv) sensor networks for real-time data acquisition; (v) AI-enhanced real-time forecasting platforms and (vi) adaptive governance for climate-resilient hybrid structural and non-structural interventions. By consolidating existing knowledge and articulating clear future research priorities, this contributes a comprehensive and transferable framework for understanding, modelling, and adapting to compound flood risk in a warming world.