The identification of risk factors for preterm labor is an important predictor. The risk factors for preterm labor can be maternal characteristics, namely maternal obstetric history, maternal body mass index and weight gain, multiple pregnancy, maternal infections, periodontal disease, maternal vitamin D deficiency, and lifestyle. Nowadays, various accurate diagnostic methods have been developed to diagnose preterm labor, namely ultrasound (cervical length, cervical consistency, uterocervical angle, and fetal adrenal gland) and biomarkers (IL-6 and IL-8 in cervicovaginal fluid, Placental Alpha Microglobulin-1 (PAMG-1), and Insulin-Like Growth Factor Binding Protein-1 (IGFBP-1), Vascular Endothelial Growth Factor (VEGF), Placental Growth Factor (PGF), Soluble VEGF Receptor-1 (sFlt-1), High Mobility Group Box-1 (HMGB1), and calponin. Artificial Intelligence was developed to predict preterm labor, namely in the form of ultrasound software which is capable of detecting cervical funneling processes ranging from resembling the T, Y, V, and U-shaped. This software is expected to be easily used by general practitioners and obstetricians and gynecologists, especially those who work in rural areas.