Premature rupture of membranes (PROM) remains a leading contributor to preterm birth and neonatal morbidity. Despite advances in obstetric care, accurately predicting PROM and its consequences continues to pose clinical challenges. This study evaluates a dual approach combining biochemical markers—particularly fetal fibronectin (fFN)—with machine learning algorithms to enhance diagnostic accuracy and risk stratification. A prospective cohort of 67 term pregnancies was analyzed for fFN presence on admission (Test 1) and on day six (Test 2), with delivery within five days used as the primary outcome. Admission fFN demonstrated excellent sensitivity (100%) but limited specificity (0%), indicating its potential as a rule-in rather than rule-out test. Conversely, Test 2 lacked predictive value. In parallel, we developed predictive models on a separate dataset (n = 135) incorporating clinical variables (maternal age, gestational age, cervical length, dilation, contractions). Random Forest models achieved high classification performance (AUC = 1.0), identifying cervical dilation, gestational age, and cervical length as the most predictive features. However, the perfect AUC suggests potential overfitting and emphasizes the need for validation. This integrated approach supports the use of admission fFN testing alongside explainable AI models as a promising framework for personalized obstetric management of PROM. Future studies should focus on larger, multi-institutional datasets and incorporate additional risk factors such as stress biomarkers and inflammatory profiles to further improve predictive accuracy.