Artificial intelligence can aid in effective aquatic ecosystem management, and advance sustainable water management techniques through several modelling tools. This review chapter examines AI applications in aquatic ecosystem monitoring and management, focusing on the effects of water contamination by different algal species. Accelerating the detection of toxic algae might lead to the development of an early warning system that could predict the occurrence and location of blooms. AI has the potential to make ecosystem recovery plans more effective. AI uses machine learning methods to improve prediction models to provide accurate and timely notices of algal blooms. As always, the major goal is to reduce the negative effects of harmful algal blooms on aquatic communities, and ecosystems by providing decision-makers with useful information for anticipatory and effective response plans. AI-based image processing algorithms have significantly advanced monitoring and forecasting of algal blooms which is crucial to reducing the harmful effects of eutrophication. While promising solutions are available to tackle such environmental concerns, their implementation becomes unsustainable when most of the challenges remain unaddressed. More research is needed to address the challenges of using AI technologies independently, including their implementation, to safeguard environmental sustainability and the importance that AI can create for current and upcoming generations. By examining various progressive applications, it will advance our understanding of how AI could be a key factor in ensuring a future that is ecologically conscious for aquatic ecosystem management.