In practical engineering, the sludge line of wastewater reclamation plants faces challenges including high parameter sensitivity, drift nonlinearity, and significant time lag, which introduce considerable uncertainty into the operation and scheduling of the sludge line and restrict the synergistic capacity of pollution and carbon reduction. Conventional mechanism-driven models require a large amount of high-quality and well-annotated input data, making it difficult to predict sludge line parameters accurately and efficiently. To enhance the synergy of pollution and carbon reduction for the sludge line in wastewater reclamation plants, this paper established a prediction model based on the multi-head self-attention mechanism (Transformer) using a hybrid modeling strategy of "empirical model baseline and residual prediction", based on the operational data of the water line and sludge line of a wastewater reclamation plant in Beijing. Specifically, this paper first generated a baseline through linear fitting of an empirical model and then utilized the Transformer model to accurately predict the residual. Taking the inlet and outlet water quality indexes and operating parameters of the water line as inputs, the model simultaneously predicted the dewatered sludge output and sludge moisture content and compared its performance with LightGBM, block recurrent neural network BlockRNN (LSTM) and temporal convolutional network (TCN) models. The results indicate that relying on the global temporal correlation capture capability of the Transformer model based on multi-head self-attention mechanism, it can effectively adapt to the nonlinear fluctuation characteristics of sludge line operating parameters and achieve the optimal comprehensive performance: R² of dewatered sludge output prediction reaches 0.778 8, and that of sludge moisture content prediction reaches 0.679, both of which are significantly higher than those of the comparative models. Meanwhile, the backtesting mechanism can improve the operational stability of the model, continuously enhance its prediction accuracy, and thus adapt to the long-term time series prediction scenarios in practical engineering. This model can realize high-precision prediction of key parameters in the sludge line, provide reliable technical support for scenarios such as real-time regulation of the sludge line process, precise chemical dosing, and intelligent equipment scheduling in wastewater reclamation plants, and facilitate the synergistic governance of pollution and carbon reduction for the sludge line.