Aquatic ecosystems are vital for biodiversity, freshwater availability, and ecological balance. However, these ecosystems face growing threats from pollution, climate change, habitat degradation, and overexploitation. Traditional monitoring methods, such as manual sampling and laboratory analysis, often lack scalability, real-time capabilities, and efficiency. Machine learning (ML) has emerged as a powerful tool for addressing these challenges by leveraging vast datasets, including remote sensing imagery, sensor networks, and ecological surveys, to detect patterns, predict environmental changes, and optimize decision-making. This chapter explores the role of ML in aquatic health prediction, highlighting its key applications in water quality monitoring, early detection of ecological disruptions, and sustainable aquaculture management. Additionally, it examines different ML techniques used in aquatic research. Despite its advantages, ML adoption faces challenges such as data availability, model interpretability, and computational constraints. Interdisciplinary collaboration among environmental scientists, data engineers, and policymakers is essential for advancing ML-driven aquatic health solutions. Researchers can create an adaptive, data-driven strategy for environmental conservation and sustainable water resource management by integrating ML into aquatic ecosystem management.