The widespread use of salicylic-acid-containing skincare products has increased reports of irritation and allergy, revealing the lack of a rapid, low-cost, and user-friendly detection tool. This study develops an educational, eco-friendly, and quantitative colorimetric method to determine salicylic acid (SA) concentrations in commercial facial cleansers. The principle is based on the formation of a purple Fe³⁺–salicylate complex under acidic conditions. Experimental optimization determined the ideal pH = 2 and Fe³⁺ dosage of 150 μL (0.1 M), establishing a strong linear calibration curve between absorbance at 520 nm and SA concentration (R² = 0.9993) across the 0.2–2.0 % range. Matrix interference from ingredients such as citric acid and EDTA caused false-negative results by competing for Fe³⁺ chelation; therefore, a filter-paper-based colorimetric test strip was developed. By varying the buffer-to-iron ratio, the optimal formulation (1:4) provided clear and stable color differentiation among 0 %, 0.22 %, 0.45 %, 0.5 %, and 2 % SA samples. Objective quantification was achieved through RGB and grayscale analysis under a fixed 400 lux light source, enabling the construction of digital color indices for potential smartphone-app integration. To further reduce cost and enhance sustainability, ferric ions extracted from discarded hand warmers were used as a green alternative to commercial reagents. The iron oxide from hand warmers dissolved in dilute hydrochloric acid effectively generated Fe³⁺ capable of reproducing comparable calibration curves and chromatic intensity, validating the feasibility of waste-derived iron as a reagent source. The proposed method demonstrates high sensitivity, reproducibility, and low material cost while promoting resource recycling and green chemistry concepts. It offers a promising prototype for home-use cosmetic safety testing, science education, and citizen-science applications. Future work will refine the test strip’s stability, extend its detection range, and incorporate smartphone-based image analysis for real-time quantitative evaluation and potential commercialization.