This study presents a multi-stage hybrid deep learning and optimization approach to predict and classify the Water Quality Index (WQI). The goal was to identify a single process to create a model pipeline that will allow for the use of both feature selection and the use of deep learning combined with metaheuristics in the assessment of water quality. The objective of this research was to develop a 3-stage predictive model using Relief-GWO feature selection, optimized using ANN/DANN with ISLO and DNN using PSO or CCPSO or a Chaos-Driven Batalgorithm. Eighteen physical, chemical, and biological characteristics of water from 3,673 sampling sites were studied. The results obtained by the baseline models developed in Phase 1 showed that SVM was the best classifier at a rate of 91.29%.Phase 2 hybrid ANN-Maxout obtained an overall classification accuracy of 93.74%, whereas DANN-ISLO achieved a better regression R² value of 0.9806 than all other models. In phase 3, the DNN-CCPSO model had the best performance for regression as measured by the R² value of 0.9813, and DNN-ChaosBat was the best in terms of F1-Macro at 0.7269. Overall, this study demonstrated that a proposed multi-hybrid approach will provide superior results to many individual models used alone, providing significant improvements over time throughout each phase. The work presented here demonstrates how the use of artificial intelligence can be applied to support automatic water quality monitoring, detect contamination early on, and support governmentdecision-making relative to environmental policies.