ABSTRACT Monitoring pollution of shallow marine ecosystems provides the key to solving environmental problems; however, traditional methods based on field surveys and ship‐based data collection are limited by high costs, low accuracy, and poor scalability. In this systematic literature review, we evaluate recent advances in integrating artificial intelligence (AI) and remote sensing (RS) technologies for monitoring marine pollution, focusing on the prevailing challenges in conventional technologies, such as limited data access, ineffectiveness of AI models, inflexibility of approaches, and inadequacy of real‐time capabilities. The novel contribution of the study lies in the synthesized review of recent advancements in AI‐based RS of various features that were analyzed using emerging AI models, identification of emerging trends, and provision of research perspectives on future improvements to increase research performance in the domain using citizen science, open‐source data, and other methods. It offers a thorough outline of cutting‐edge approaches, identifies particular weaknesses in existing monitoring systems, and provides novel solutions to meet these challenges, improving the scalability, efficiency, and accuracy of the shallow marine monitoring strategies.
Sartono et al. (2025) studied this question.