Preterm birth rates have been increasing over the past several years. Currently, there are no accurate methods to predict when and if a woman will go into labor. This work developed a framework to create patient-specific parametric models of the uterus and cervix from 2D ultrasound images and cervical stiffness measurements to investigate mechanical factors surrounding preterm birth during gestation. The goals of this research are (1) to collect longitudinal data on maternal anatomy throughout pregnancy, (2) to develop a framework for the computational study of pregnancy biomechanics through verification as an in-silico mechanical test of maternal anatomy, and (3) to use the framework to investigate the mechanical factors that lead to preterm birth. Though extensive knowledge exists on how the fetus normally grows and develops during pregnancy, very little is known about normal growth and change in maternal reproductive tissues. The lack of knowledge on what constitutes a normal progression in maternal reproductive tissues thus makes it difficult to diagnose patients who are progressing abnormally, as is the case for preterm birth. Though several of the main reasons cited for preterm birth are mechanical in nature, the normal mechanics of the uterus, cervix, and fetal membrane are not well understood. This work quantifies changes to maternal reproductive anatomy across gestation via the collection of ultrasonic dimension measurements in patients who are considered at low- and high-risk for preterm, along with measurements of cervical stiffness. These measurements provide the basis for parametric patient-specific computational models of pregnant maternal reproductive anatomy. Because pregnancy is a protected environment, it is not possible to study the mechanical loading of maternal tissue in-vivo. Computational simulations of maternal anatomy have the potential to shed light on the mechanics of the gravid environment. Previous methods of generating maternal anatomy have relied on magnetic resonance images or 3D ultrasounds, which are not standard during prenatal care. Thus, the method of capturing measurements of maternal anatomy via 2D ultrasound images and parametric approach to generating solid models of maternal anatomy for use in finite element analysis provides a clinically implementable way to study pregnancy biomechanics. This work develops a workflow for generating solid models of maternal anatomy that capture the shape of the uterus and systematically verify the modeling approach in computational simulation to use as patient-specific in-silico mechanical tests. With the patient-specific data on maternal anatomy collected and the verified approach to computationally simulate maternal tissue loading, this work investigates the differences in the maternal tissue loading for patients at low- and high-risk for PTB. This was accomplished by qualitative and quantitative observation of stretch in the proximal cervix face. Quantitative comparisons were also made between patients who did and did not deliver preterm of stretch in the proximal cervix. The culmination of this work significantly adds to the knowledge of the mechanical environment of pregnancy and develops the basis for in-silico study of preterm birth, as well as at-term study, providing a basis for future study on pregnancy biomechanics.