The Strongford case study demonstrates the development and application of an advanced Digital Twin to support real-time prediction, monitoring, and mitigation of N₂O emissions. Aligned with Severn Trent’s Net Zero strategy, the Digital Twin integrates process knowledge, high-frequency operational data, and AI/ML-based analytics to predict ammonia, N₂O risk and emissions across different aeration lanes and zones without compromising effluent compliance. Knowledge-based AI, informed by peer-reviewed literature, is used to identify N2O formation risk and operating conditions associated with increased emissions, with results showing a strong correlation between predicted risk and measured N₂O concentrations. Seasonal analyses highlight low dissolved oxygen (DO) during ammonia peaks as a dominant driver of N₂O formation, providing a clear basis for targeted operational intervention. Model performance meets established predictive emissions monitoring system (PEMS) criteria, achieving relative accuracies below 20% and improving with increased multi-season training data and cross-lane validation demonstrates model transferability. By combining N₂O prediction with load-based DO control, the system generates optimized, zone-specific DO setpoint recommendations that balance energy use, effluent quality, and greenhouse gas reduction. Overall, the Strongford Digital Twin illustrates how AI/ML decision support can be practically embedded into wastewater operations to streamline N₂O mitigation and support sector-wide decarbonisation objectives.