This study explores the use of Machine Learning to track anxiety in pregnant women through voice analysis of data collected in a maternal health initiative, processed with MFCC, OpenSMILE, and PyAudio in 8-second windows. Ten classification algorithms were initially tested, with five thoroughly evaluated using AUC, F1-score, and accuracy. Logistic Regression using PyAudio features yielded the best results (AUC=0.705, F1-score=0.51), and demographic data improved performance, while ensemble methods may enhance stability, despite the limited dataset. Although promising, these findings require validation on a larger dataset, and the modeling will be refined for robustness and clinical applicability. These results suggest voice analysis represents a promising approach for anxiety screening in pregnancy, offering a potentially accessible and non-invasive tool for early identification and support.