The control of biogas production in anaerobic digestion systems is inherently challenging due to pronounced nonlinear dynamics, biological uncertainty, long time delays, and limited availability of reliable online measurements. In response, a range of advanced control strategies—including model-based, data-driven, and artificial intelligence-assisted approaches—have been proposed to enhance methane productivity while maintaining process stability. Nevertheless, a systematic and quantitatively grounded comparison of these strategies from an application-oriented perspective remains limited. This study presents a comparative assessment of novel biogas control strategies using a Multi-Criteria Decision Analysis (MCDA) framework. The evaluated alternatives include fuzzy supervisory control, adaptive neuro-fuzzy inference systems (ANFIS), mechanistic model predictive control (MPC), data-driven MPC employing machine-learning predictors, reinforcement learning-based control, and hybrid architectures that integrate soft sensors with intelligent supervisory layers. Eight evaluation criteria were defined to reflect the requirements of full-scale anaerobic digestion systems, including stability and risk prevention, methane productivity, constraint handling capability, robustness to feedstock variability, sensor practicality, implementability in PLC/SCADA environments, explainability, and lifecycle effort. The MCDA results indicate that hybrid strategies combining soft sensing with supervisory control achieved the highest aggregated performance score (0.82 on a normalized scale), followed by fuzzy (0.76) and ANFIS-based (0.74) supervisory controllers. MPC-based strategies exhibited superior constraint handling performance (criterion scores above 0.85) but were comparatively penalized due to higher modeling and implementation effort. The reported literature suggests that data-driven predictive control can improve methane yield by approximately 5–10%, while intelligent supervisory control supported by soft sensors may reduce acidification risk indicators by 20–30% relative to baseline operation. Reinforcement learning approaches demonstrated high theoretical optimization potential but the lowest industrial readiness. Overall, the proposed MCDA framework highlights hybrid intelligent control architectures as the most balanced solution, offering a practical compromise between performance enhancement, robustness, and deployability in biogas production systems.