Artificial Intelligence-Augmented Approaches for Next-Generation Harmful Algal Bloom Management

Harmful algal blooms (HABs) are escalating global threats to aquatic ecosystems, water security, and public health. Although conventional monitoring and management approaches, ranging from in situ sensing to predictive ecological models, have advanced bloom detection and risk assessment, their effectiveness is often constrained by data scarcity, transferability, and real-time applicability. Artificial intelligence (AI) offers transformative capabilities across the HAB management continuum, from detection to decision support, positioning AI as a cornerstone of next-generation strategies to mitigate bloom risks. This review synthesizes recent progress in AI applications, including automated phytoplankton identification, remote sensing analysis, predictive modeling, and decision-support systems, and evaluates classical machine learning, deep learning, and automated machine learning (AutoML) approaches. This review highlights how integrating AI with conventional ecological knowledge and expert judgment can yield adaptive, scalable hybrid intelligence frameworks. By integrating technological innovation with established monitoring practices, next-generation HAB management can shift from reactive responses to proactive strategies.