Anaerobic digestion (AD) is a well-established pathway for treating agricultural organic waste, and machine learning has emerged as a novel tool to predict its product performance. In prior research, the majority of studies concentrated on non-time series models for laboratory-scale fermentation data. Consequently, the generalization performance of these models was significantly constrained, particularly in the context of industrial-scale biogas projects. Thus, in this study, typical non-time series models (GBR and RF) and time-series models (LSTM, CNN-LSTM, and DA-LSTM) after hyperparameter optimization were chosen to accurately predict the characteristics of digestion products in a biogas project. The ideal GBR model for CH4 content was obtained, and the R2 values of the test set and training set were 0.93 (RMSE=1.11) and 0.97 (RMSE=0.69), respectively. Temperature was the most important parameter for biogas production according to feature importance and SHAP analysis of the RF model. The DA-LSTM was superior to LSTM and CNN-LSTM for the prediction of biogas production, and the R2 of DA-LSTM was 0.87 (RMSE=1048.14) with a seq_len of 10 d. This study provides direction for high-efficiency biogas production in wet biogas projects with the aid of reliable machine learning models.