Predicting water quality is crucial for managing water resources and preventing pollution. Predictive analytics research for different water bodies in delta environments has not kept up with environmental demands. This chapter fills this gap by doing a thorough study and predicting water quality measurement data from numerous dispersed water bodies, particularly inside the delta regions. A case study in South India’s delta region was also examined to forecast the aquaculture zone’s water quality utilizing machine learning and deep learning methodologies. Ammonia levels in aquaculture water bodies were predicted using random forest (RF), support vector machines (SVM), convolutional neural networks (CNN), and forward neural networks (FNN). The study’s empirical findings demonstrate the system’s exceptional forecasting capabilities. This chapter provides particular recommendations for water quality monitoring, treatment, and management strategies based on these findings, which are adapted to the unique requirements of the aquaculture zone in the delta regions. These contributions can potentially help environmental managers and policymakers make better decisions.