In general, water resources are much significant for human life and economical improvement, this is more significant in public health and environmental sustainability. Recently, predicting water quality has become a key research area in the field of ecological environmental science, with significant implications for preventing water pollution and establishing automated water quality monitoring systems. Moreover, classical water quality prediction schemes often face several limitations while dealing with complex, large-scale environmental data. To bridge this gap, an efficient model is required to make a comprehensive review for distributed learning-based aqua quality prediction. This research provides a comprehensive analysis of traditional water quality prediction methods and offers a technical overview of their application in predicting water quality. It presents an in-depth review of the current state of water quality prediction, highlighting the key characteristics of emerging technologies in this field. The study examines 20 research articles that focus on various methodologies used for water quality prediction within the context of distributed learning. Finally, the analysis is organized through a survey that evaluates the articles based on publication year, research techniques, performance metrics, toolset, and utilized database.