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