Tighter global emissions standards demand innovative solutions for precise exhaust gas analysis. Traditional NOx sensors, while effective in diesel engines, often struggle with the unique challenges of gasoline engines, creating a pressing need for alternative approaches. This study introduces an AI-based virtual sensor as a groundbreaking solution for NOx measurement, utilizing readily available on-board diagnostics (OBD) signals to eliminate the need for costly additional hardware. A 4-cylinder gasoline engine equipped with advanced measurement instrumentation was tested on the road and validated on a roller test bench and an Engine-in-the-Loop (EiL) bench, complemented by an exhaust gas analysis system for detailed NOx profiling. Leveraging the predictive power of an XGBoost machine learning model, key parameters influencing NOx emissions were identified and incorporated into a robust virtual sensor model. Validation was conducted using chassis dynamometer data from a vehicle with a similar engine configuration, confirming the model’s predictive accuracy across real-world operating conditions. The findings highlight the critical importance of achieving accurate and reliable NOx measurement results to meet future emission standards. The proposed virtual sensor not only enables cost-effective NOx analysis but also provides a means to validate existing sensor data and ensure plausibility. Additionally, it offers a robust fallback solution in the event of sensor failure, ensuring uninterrupted compliance with regulatory requirements. This study demonstrates a methodology for training virtual NOx sensors, highlighting their strengths and limitations. It provides valuable insights into the potential of virtual sensing technologies to complement or replace conventional sensors, laying the groundwork for future advancements in emissions measurement and control.