In the anaerobic digestion (AD) process there are some difficulties in maintaining process stability due to the complexity of the system. The variability of the raw material coming to the facility, temperature fluctuations and pH changes as a result of microbial processes cause process instability and require continuous monitoring and control. Increasing continuous monitoring, and internet of things applications within the scope of Industry 4.0 in AD facilities can provide process stability control and early intervention. In this study, five different machine learning (ML) algorithms (RF, ANN, KNN, SVR, and XGBoost) were used to describe and predict the correlation between operational parameters and biogas production quantities collected from a real-scale anaerobic digestion plant. The KNN algorithm had the lowest accuracy in predicting total biogas production over time, while the RF model had the highest prediction accuracy of all prediction models. The RF method produced the best prediction, with an R2 of 0.9242, and it was followed by XGBoost, ANN, SVR, and KNN (with R2 values of 0.8960, 0.8703, 0.8655, 0.8326, respectively). Real-time process control will be provided and process stability will be maintained by preventing low-efficiency biogas production with the integration of ML applications into AD facilities. Display omitted •Industrial Scale anaerobic digestion plant data were collected.•5 different ML algorithms were used to predict biogas production.•The best prediction was achieved with the RF algorithm (R2 = 0.9242).•The model with the lowest R2 value (0.8326) was the SVR algorithm.•Process stability could be maintained with ML applications in AD plants.