The sulfate reduction-sulfur autotrophic denitrification-nitrification (SANI) process is a novel biological wastewater treatment technology for nitrogen removal, which has advantages such as low sludge yield, high temperature tolerance, and salt tolerance. However, the complex microbial communities within this process are extremely sensitive to operational parameters, leading to easy fluctuation of system performance, which urgently requires reliable modeling tools for optimization. Traditional mechanistic models are cumbersome to construct and have limited universality, while machine learning methods are often limited by small sample size, data imbalance, and insufficient interpretability. To this end, this paper proposed a new data-driven method integrating generative adversarial network (GAN) and staged machine learning modeling (including XGBoost, Artificial Neural Network, and Support Vector Machine algorithms), constructed two-stage prediction models for anaerobic sulfate reduction and anoxic sulfur autotrophic denitrification according to the biochemical mechanism of the process, and integrated acclimation and operation data to expand the applicability of the models. The results show that the root mean square error (RMSE) of the model for the prediction of COD and nitrogen removal rates are as low as 6.8% and 5.6%, respectively, exhibiting excellent prediction accuracy. The interpretability analysis further clarifies the key environmental factors and optimal control ranges of the process: influent COD of 340-500 mg/L, influent sulfate (as S) of 300-450 mg/L, anaerobic effluent sulfide (as S) of 50-100 mg/L, anaerobic working volume of 5-6.5 L, and hydraulic retention time of 6-8 h. This paper provides a reliable tool for the precise regulation of the SANI process and further constructs a transferable and scalable integration paradigm of "data augmentation, staged modeling, and mechanism interpretation", which provides a brand-new methodological foundation for the intelligent optimization of complex biological wastewater treatment processes.