The prediction of performance indicators such as Volatile Solids Reduction (VSR) is key for the accurate sizing of municipal sludge digesters. However, prediction models available from the literature typically lack accuracy, leading to the use of conservative values and inadequate process sizing. The present study aims at developing an empirical model for VSR prediction. This model is based on over 32 years of operational data originating from six industrial plants featuring a diversity of wastewater treatment lines. The model was benchmarked against the Anaerobic Digestion Model No. 1 (ADM1) with or without calibration and displayed the lowest average prediction error (RMSE of 4.2%VSR) and bias (2.3 %VSR). These high performances were obtained when predicting VSR for plants, independent from the empirical model training data (i.e., unseen data), highlighting its applicability for design tasks. During the design phase, the proposed model can promote optimal sizing of biogas networks and ancillary equipment, reducing inefficiencies and operational costs. For operational optimization, the model supports dynamic sludge age control strategies to maximize methane yield, contributing to sustainable wastewater treatment practices. In the future, the proposed model can still be improved upon by integrating new operational data with the already available training data used in this study.