This study primarily focuses on the analysis of volatile organic compounds using GC–MS, with ICP-MS employed as a complementary method to quantify trace metal content. Headspace GC–MS was conducted to detect alkylphenol ethoxylates (APEOs), formaldehyde, aromatic amines derived from azo dyes, perfluorinated carboxylic acids, chlorophenols (PCPs), tetrachlorophenols (TPCs), and phthalates in textile samples of different origin and composition. Principal component analysis was used to detect patterns in the volatilome according to the origin and the textile composition. In addition, seven metals (Cr, Ni, Cu, Mo, Cd, Hg, and Pb) were quantified in a subset of samples. The study revealed distinct chemical profiles in textiles based on their origin, with GC–MS identifying key volatile organic compounds and ICP-MS quantifying heavy metals in a subset of samples. Principal component analysis highlighted cotton content as a critical factor in differentiating textile profiles. While most samples adhered to regulatory standards, some exceeded thresholds for metals like copper and nickel, underscoring the need for enhanced quality control in manufacturing processes. By integrating advanced analytical methods, this study provides insights into sustainable and safe textile production, offering valuable benchmarks for regulatory compliance and industry best practices. The outcomes contribute to improving product safety, promoting responsible manufacturing, and supporting regulatory bodies in the enforcement of environmental and safety standards, aligning with the growing demand for sustainability in the textile sector.