Aiming at the problems of low model prediction accuracy caused by the temporality, strong nonlinearity, and high dimensions of water quality data, a water quality prediction model based on the IGJO-TCN-BiGRU-SA has been proposed. Firstly, missing data are repaired through the CSI algorithm, while abnormal data are removed through Boxplot and FFT algorithms to enhance data completeness. The GRA algorithm is used to analyze the correlation of water quality indicators and obtain strongly correlated water quality indicators. Secondly, the BiGRU model is employed as the baseline model to extract latent change features from the data. The TCN algorithm is employed for local feature extraction from the data, enhancing the model’s capability to extract features at different time scales. The SA mechanism is used to adaptively update the feature weights of the BiGRU model, effectively extracting key features from global contextual information. Finally, the IGJO algorithm is employed to optimize the L2 of TCN, the learning rate and neuron number of BiGRU, and the key of the SA mechanism, thereby improving the accuracy of water quality prediction. The historical water quality data from the Huai River Basin were used as samples for experiments. Compared to classic algorithms such as LSTM, GRU, BiGRU and TCN-BiGRU-SA, the IGJO-TCN-BiGRU-SA prediction algorithm proposed in this research achieved reductions of 19.64% to 68.29% in MAE, 25.47% to 75.19% in RMSE, 28.71% to 73.36% in MAPE, and 0.19% to 3.61% in R2. Experimental results indicate that the water quality prediction algorithm based on IGJO-TCN-BiGRU-SA significantly enhances the model’s predictive accuracy.