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Abstract: The preservation of biodiversity and water quality is high priority. The advanced monitoring is necessary for the growing threat posed by pollutants such as plastics, oil spills, and toxic plants in aquatic ecosystems. Conventional techniques, which depend on manual inspection, are timeconsuming and inadequate for the pollution problems of today. Deep learning and machine learning are used as revolutionary technologies for quick and precise contaminant detection. It turns to innovative techniques such as transfer learning for identifying toxic plants, semantic segmentation for mapping plastics using UAV data, and YOLO, for real time object detection algorithm using Convolutional Neural Networks (CNNs) for oil spill identification in SAR imagery. The study summarizes recent findings and highlights uses such as algal bloom prediction and real-time river plastic monitoring. For decentralized, real-time analysis, it suggests hybrid systems that combine satellite and real-time imagery with advanced methods like edge AI and federated learning.