Due to the alteration of coagulatory and fibrinolytic proteomes during pregnancy, the human body tends to lean toward a hypercoagulable state. As a result, pregnant and postpartum women have at least 4 times higher risk of developing thromboembolism, hemorrhage, and other bleeding disorders in comparison to non-pregnant people. Moreover, pregnancy-related deaths in the United States have faced an uptick, increasing more than two-fold between 1987 and 2019. This alarming rise in coagulopathic morbidities and mortalities could be attributed to the inability of routine screening assays to identify abnormalities in hemostatic pathways. As a result, the development of robust and effective tools for prognosis and treatment is a necessity. Towards this goal, mathematical modeling principles have been widely adopted, enabling the investigation of conditions that may be hard to emulate experimentally. While traditional kinetic models have been largely popularized for the study of biochemical networks, these frameworks possess disadvantages that affect accuracy and generalism. As a result, power-law formalism, which presents generic model descriptions and yields a reduced system of non-linear ordinary differential equations, has become an area of interest since the 1960s. The development of accurate lower-order models of coagulation and fibrinolysis would potentially streamline the modeling process and enable easier diagnosis and treatment of coagulopathies. Toward this goal, this work uses the biochemical systems theory framework, which uses power laws, to describe coagulatory and fibrinolytic pathways in pregnant patients, thereby quantifying hypercoagulability at various stages of pregnancy. Next, this work also explores coagulation in bleeding disorders such as hemophilia. Taken together, we have developed a robust and uninvolved model of coagulation and fibrinolysis that could find use in many applications.