Textile dyeing industry is a significant contributor of complicated and extremely polluting wastewater. This wastewater has intermittent loads of chemical oxygen demand (COD), stains and other pollutants which puts dangerous effects on the sustainability of the environment and human beings in general. The traditional operation of wastewater treatment plants is reactive and rule-based to a large extent. These methods are ineffective in dealing with the non-linear dynamic character of the effluent of the textile business, resulting in low efficacy and recurring regulatory breach. To overcome these shortcomings, this paper will suggest a new hybrid architecture SAGE-GBTCN (Shock-Aware Gated Ensemble with Gradient Boosting and Temporal Correction Network) to be used in the effective prediction of wastewater pollution. This model combines a gradient boosting ensemble to produce baseline predictions and a parallel temporal network with a residual correction. A shock-sensitive gating system is used to dynamically modify the correction process to consider any sudden, non-stationary changes in the nature of the effluents. This design makes the model very useful in capturing the long-term trends as well as abrupt disruptions within textile wastewater. The suggested SAGE-GBTCN model was tested with the help of data on a full-scale wastewater treatment facility. The findings are shown to be more accurate in prediction and better resistant to abnormal operating condition. The model also demonstrates high possibilities to facilitate active and energy saving management of textile wastewater treatment processes, which will result in an R2 predictive value of 0.942 and a RMSE of 30.30 of COD. Although validated on full-scale industrial WWTP data, the proposed framework targets operational characteristics typical of textile effluent treatment plants, including batch-wise COD loading, abrupt shock events, and chemically driven variability.