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Agarwood oil, a valuable product in perfumery and traditional medicine, faces quality inconsistencies due to the lack of a standardized grading system. This study introduces SmartAgar, an AI-driven approach using the NARX neural network to classify oil quality into six refined grades. Trained with chemical compound data from FRIM and BARCE-UMPSA, the model is optimized in MATLAB for performance using LM algorithm. Evaluation metrics such as MSE, RMSE, MAE, and R² confirm high accuracy and reliability. SmartAgar offers a scalable, data-driven solution to enhance quality assurance and promote consistency in premium Aquilaria oil classification.