Ecstasy is a recreational drug known to have serious adverse effects, necessitating the development of new techniques for its rapid and affordable identification in harm reduction policies and forensic laboratories. In this study, ATR-FTIR spectroscopy and DD-SIMCA (Data Driven Soft Independent Modeling of Class Analogy) were employed to determine the profile of ecstasy tablets and their adulterants. ATR-FTIR spectroscopy, which is a fast and non-destructive method that provides structural information, and DD-SIMCA, a supervised technique that distinguishes between different groups, were used. The Hierarchical Cluster Analysis (HCA) revealed the formation of two primary clusters: one comprising mainly MDA samples with some MDMA samples, and the second including cocaine, methamphetamine, and caffeine samples, as well as a mixture of MDA samples with methamphetamine and caffeine. Principal Component Analysis (PCA) was performed, and the first five principal components (PC) explained 86.25% of the total data variance. In the PC1xPC2 scores plot, the distribution of samples containing MDs (MDA and MDMA) was observed. Further analysis using PCA revealed the grouping of cocaine samples into two separate groups based on variations in concentration. The DD-SIMCA model demonstrated high sensitivity values in accurately identifying target samples (MDA) and relatively high specificity values in both the training and test sets. The study highlights the effectiveness of ATR-FTIR spectroscopy, PCA, and DD-SIMCA for the precise classification of ecstasy and its adulterants and suggests their potential in drug identification and analysis.