In this study, an integrated hurdle modelling workflow combining ATR-FTIR and machine learning was developed for the simultaneous quantification of levodopa (LD), carbidopa (CD), and benserazide (BZ) in anti-Parkinson's medication. A calibration set of 103 synthetic pellets was prepared with wide concentration ranges for the three active pharmaceutical ingredients (APIs), 0-80% w/w for LD, 0-40% w/w for BZ; and 0-8% w/w for CD, and analysed by ATR-FTIR and HPLC-DAD reference method. Nine pellets prepared from three commercial medications were used for external evaluation and calibration transfer. A two-stage hurdle model was employed for each analyte, including a classifier for presence/absence and a regressor fitted only on positive samples to predict concentration. The modelling workflow comprised four modules, in which the Savitzky-Golay first derivative, combined with the standard normal variate (SNV) on mean spectra, was the most suitable preprocessing method. Logistic regression for LD and BZ, and supported vector classification (SVC) for CD were selected as optimal classifiers, while random forest (RF) regression in the half spectral region (675-1800 cm-1) was the best overall regressor for all APIs. When applied to commercial samples, the model classifiers correctly identified the presence/absence of all APIs, and calibration transfer using only six additional commercial pellets significantly reduced bias for all three APIs and achieved a high AGREE green score (0.75) at the same time. These results show that ATR-FTIR, combined with a carefully designed hurdle model workflow, can provide a rapid and green screening tool for multi-API in anti-Parkinson's medications.