Biogas production through anaerobic digestion (AD) of industrial organic waste and wastewater offers a sustainable method for energy recovery. However, since process efficiency heavily relies on operational factors, continuous monitoring of the AD process and the implementation of necessary operational strategies are crucial. In recent years, the use of machine learning techniques (ML) has become widespread for analysing the effects of operational factors on anaerobic digestion efficiency. Among these, Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel has been used to predict biogas yield based on diverse operating parameters. This study aimed to investigate the predictability of changes in biogas production using the SVR algorithm with an RBF kernel in a full-scale anaerobic digester treating wastewater from a fruit processing plant. In the model, biogas production was estimated based on variations in selected operational parameters, achieving a regression coefficient (R²) of 0.8983 ± 0.03 with mean square error (MSE) of 0.0047 ± 0.0017. The model's performance was evaluated using 10-fold cross-validation techniques and relevant statistical indicators to ensure robustness and generalisability. Hyperparameter tuning was conducted to enhance prediction accuracy while reducing model error. The findings demonstrated that ML-based modelling can serve as a reliable and effective tool to improve biogas production efficiency in wastewater treatment applications. Furthermore, the study highlights the potential of such models to support real-time process control and decision making in anaerobic digestion systems operating under variable industrial conditions.