This study presents a novel methodology for benchmarking Hydrogen Internal Combustion Engine (H2E) emissions against diesel vehicle configurations, emphasizing Real-Drive Emission (RDE) test procedures. By leveraging Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models, emission profiles for legal cycles and RDE scenarios are predicted. Integrated data pipelines and physics-based modeling enable virtual evaluations of Selective Catalytic Reduction (SCR) system performance, ammonia dosing accuracy, and exhaust temperature dynamics. Key results demonstrate high prediction accuracy across models, including temperature (R² > 0.94, RMS error <25°C), air flow (92% accuracy, RMSE = 28 kg/h), upstream NOx (93% accuracy, RMSE <10 mg/s), and SCR (TP NOx accuracy = 85%, dosing accuracy = 90%). This approach significantly reduces the need for extensive on-road driving tests, as the model performs most of the work, thereby lowering development costs and supporting OEMs in meeting stringent emission standards through efficient virtual testing of Exhaust Gas Treatment (EGT) systems.