The illegal ivory trade has led to a sharp decline in wild elephant populations in recent decades, while mammoth (Mammuthus primigenius) ivory products have increasingly flooded the marketplace, complicating identification efforts by frontline law enforcement. Existing ivory identification methods face several challenges, including inconsistent accuracy due to sample quality, high costs, slow turnaround times, and destructive sample requirements. Currently, there is a lack of a fast, convenient, and highly effective solution to address these issues. To tackle these challenges, this study developed a novel, efficient, and accurate method for identifying and characterizing ivory products using ultra-performance liquid chromatography with a fluorescence detector (UPLC-FLR). The technique focuses on various organic small-molecule compounds in ivory that have been largely overlooked. A chemical fingerprint library was created using 72 African elephant and 69 mammoth ivory samples. Comprehensive comparative analyses, including principal component analysis, compound distribution analysis, proportion analysis, chord diagrams, and statistical significance tests, revealed significant differences between the organic compounds in the two sample groups, confirming the method's reliability. Six widely used machine learning classification models were then applied to construct a discriminant model based on 11 key feature compounds among the 85 identified, with each model achieving 100% classification accuracy. Compared to the conventional 'gold standard' molecular biology method, this UPLC-based approach shortened detection time from 24 h to just 1 h, reduced the sample requirement by 50%, and cut costs by 90%, making it a more efficient, user-friendly solution for frontline law enforcement. Widespread adoption of this method in law enforcement could become a powerful tool in the fight against the illegal ivory trade.