Naga Raju Pechetti,Mr. B. Nandana Kumar 03 09 Aquaculture productivity and sustainability are strongly influenced by waterquality conditions making continuous monitoring and timely management essential Conventional monitoring approaches often depend on periodic measurements and manual interpretation limiting their ability to identify rapidly changing conditions Recent advances in machine learning provide opportunities for waterquality prediction forecasting anomaly identification and intelligent aquaculture management This survey reviews machinelearning and deeplearning approaches for aquaculture waterquality assessment considering parameters such as pH temperature dissolved oxygen turbidity and related environmental indicators Existing approaches including traditional machine learning neural networks hybrid models timeseries forecasting and sensorassisted monitoring are examined with respect to their capabilities and limitations Based on the surveyed literature an integrated AIbased aquaculture recommendation framework is presented that connects data preprocessing waterquality prediction anomaly detection and managementoriented recommendations The survey further discusses evaluation requirements research challenges and future directions toward intelligent adaptive and sustainable aquaculture systems Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026