The adulteration of high-value edible oils, such as walnut and pumpkin seed oils, with lower-cost alternatives like sunflower oil undermines product authenticity and misleads consumers. This study presents a reliable and cost-effective strategy to detect such adulteration through tocopherol profiling combined with multivariate statistical modeling. Tocopherols (α-, β-, γ-, and δ-) were quantified using high-performance liquid chromatography with fluorescence detection (HPLC-FLD), following a streamlined sample preparation protocol. A partial least squares (PLS) regression model was developed using tocopherol concentration data from authentic cold-pressed walnut and pumpkin seed oils adulterated with 0–50% sunflower oil. The model achieved excellent prediction accuracy (R² > 0.98, RMSE < 1.5%) and was validated using cross-validation and independent test sets. Notably, it could detect adulteration at levels as low as 2–3%, well below the commonly reported threshold of sensory or visual detection. The differential tocopherol profiles across oil types served as effective chemical markers for model training. Compared to gas chromatography-based techniques, this approach eliminates derivatization steps and reduces analysis time and cost, supporting its use in routine quality control workflows. This work demonstrates that coupling tocopherol fingerprinting with predictive modeling enables the sensitive, non-destructive authentication of premium edible oils. The approach holds potential for wider applications in regulatory and industrial settings and may contribute to future standardization efforts in food authentication.