A Critical Review of Cyanoblooms and Cyanotoxins: Risk Assessment on Human Health and Agriculture along with Mitigation Strategies Using Machine Learning Perspectives

Cyanobacterial algal blooms (CABs) are recognized as an emergent concern globally due to their undesirable impacts on water quality, agricultural ecosystems, and human health. These blooms, fueled by nutrient pollution and climate change, lead to the excessive growth of cyanobacteria, which can release harmful toxins known as cyanotoxins. The current review bestows a comprehensive overview of CABs, their environmental drivers, and the types of cyanotoxins produced. It explores the implications of these toxins for agricultural productivity, ecosystem sustainability, and public health. Furthermore, emerging mitigation approaches are discussed, including physical, chemical, and biological methods aimed at controlling bloom formation and reducing toxin release. In recent years, artificial intelligence (AI) and machine learning (ML) have appeared as powerful tools for environmental monitoring and prediction. Keeping this in mind, the present article highlights for the very first time the integration of AI/ML techniques for enhancing early detection of CABs, predicting the bloom dynamics, and optimizing the mitigation strategies. By combining traditional mitigation strategies with AI-driven insights, this paper provides a roadmap for addressing the challenges posed by CABs in a rapidly changing global environment, offering solutions that balance agricultural productivity with environmental and human safety.