<p>The residues of fluoroquinolone antibiotics (FQs) have attracted widespread attention due to their potential health risks. Given the high structural similarity among FQs, traditional methods for analyzing their mixed residues are often complex and inaccurate. This study proposed a method combining terahertz (THz) spectroscopy and machine learning for qualitative and quantitative analysis of multicomponent mixtures containing four FQs (nadifloxacin [NAD], pefloxacin [PEF], ofloxacin [OFL], and enrofloxacin [ENR]) at low mass ratios (0.067–0.333). For qualitative analysis, multi-step preprocessing (MP) and support vector machine (SVM) were integrated to develop the MP-SVM model. The MP-SVM achieved an average classification accuracy of 0.967 for the four FQs in test sets. While MP enabled high-resolution qualitative discrimination, it was insufficient for accurate quantification of complex FQ mixtures. Consequently, further optimizations of feature data and model parameters were conducted for quantitative analysis. Specifically, a high-quality feature matrix (T) was constructed by merging fingerprint features of each FQ. The sparrow search algorithm (SSA) was employed to optimize support vector regression (SVR) parameters, forming the MP-T-SSA-SVR model. This model significantly improved quantitative performance, with a coefficient of determination (<i>R</i>²) of 0.971–0.972, a root mean square error (RMSE) of 3.405–3.514, and a mean absolute error (MAE) of 2.090–2.400 in test sets. Compared to similar studies, this work involves more diverse FQs with higher structural similarity, providing a new reference for advancing qualitative and quantitative analysis of practical multicomponent mixtures.</p>