Cell-free DNA (cfDNA) fragmentation encodes biological signals with non-invasive diagnostic potential in oncology. This work develops mathematical and computational models to quantify fragmentation patterns and connect them to tissue-of-origin and disease state. Building on the Markovian FRIME process (Tsui et al.), a probabilistic framework is introduced for cfDNA fragmentation, enabling simulation of fragmentation dynamics and comparison with experimentally observed distributions. In parallel, data-driven approaches expand on these probabilistic features: machine learning classifiers (support vector machines, gradient boosting) are trained on fragment size profiles, end-motif frequencies, and nucleosome-positioning signals to distinguish between malignant and non-malignant cfDNA. This combination of mechanistic modeling and classification yields interpretable yet predictive tools. Preliminary results indicate that fragmentation-derived features effectively separate cancer and non-cancer samples, while the FRIME-based framework provides insight into the underlying processes. Ongoing challenges include validating fragmentation models across cohorts and sequencing platforms, and prioritizing clinical contexts where cfDNA fragmentation analysis offers unique advantages, such as early cancer detection or minimal residual disease monitoring. Together, these approaches highlight how quantitative frameworks can enhance the diagnostic utility of cfDNA in oncology.