Liquid biopsy has emerged as a powerful tool for cancer early diagnosis, prognosis, and treatment monitoring across a wide range of cancer types. The ability to collect circulating cell-free DNA (cfDNA) from blood samples provides real-time insights into tumor biology, enabling its application in clinical practice for cancer screening, diagnosis, minimal residual disease assessment, and prediction and monitoring of treatment response and relapse. Given the increasing complexity, volume, and longitudinal nature of cfDNA data, there is a growing demand for advanced computational modeling (CM) approaches that can transform these data into clinically actionable insights. We report on the diverse CM approaches used to analyze cfDNA in oncology. After an overview of the current data derived from cfDNA, the use of CM is detailed for their application in clinical studies, both in processing cfDNA data at a particular time point and in capturing their temporal dynamics. We emphasize on approaches using machine learning and mechanistic modeling embedded within non-linear mixed effects statistical constructs. This review provides guidance to computational modelers, clinical researchers and healthcare practitioners in effectively utilizing cfDNA data to enhance research and improve patient care.