To meet the needs of the future, marine environmental monitoring must develop methods to efficiently combine and utilise data from a diverse range of sources (e.g., satellite imagery, sensor networks, acoustic data). Generative Artificial Intelligence (GenAI) is uniquely suited to aid with this by enabling the synthesis and integration of heterogeneous and often incomplete data. Its ability to learn underlying statistical patterns supports data fusion, imputation, and enhanced interpretation across sources. GenAI also introduces novel modelling approaches to tackle ecological uncertainties and improve predictive insight. Here, we present a comprehensive overview of GenAI applications in marine ecological monitoring, emphasising its potential to improve data quality control, automate species identification, and support the creation of digital twins. We also highlight key research challenges, such as managing model bias and ensuring system transparency, and outline future directions for integrating GenAI into sustainable marine ecological monitoring and management

This work also acknowledges the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S). [...] L.O. gratefully acknowledges MUR (MInistry for University and Research), for the financial support through the project INFANZIA DIGItales3.6 and the Law 232/2016, ‘‘Department of excellence’’, Department of Agriculture and Forest Sciences (DAFNE) of the University of Tuscia (Italy). J.A and N.B. acknowledge funding from Spanish Government through the projects "Artificial Intelligence for Sea Ecological Advanced Monitoring and Restoration" (AI4SEA), AIA2025-163346-C44), and "Establishing a New Deep-Sea Observation Station with Smart Robotics for Habitat Restoration Monitoring and Fisheries Assessment" (SMART-ME), PID2024-1553440B-C31

15 pages, 7 figures, 1 table, supplementary data https://doi.org/10.1016/j.envsoft.2025.106789.-- Data availability: No data was used for the research described in the article

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