The circulating cell-free nucleic acids (ccfNAs) consist of a heterogenous cocktail of both single (ssNA) and double-stranded (dsNA) nucleic acids. These ccfNAs are secreted into the blood circulation by both healthy and malignant cells via various mechanisms including apoptosis, necrosis, and active secretion. The major source of ccfNAs are the cells of hematopoietic system under healthy conditions. These ccfNAs include fragmented circulating cell free DNA (ccfDNA), coding or messenger RNA (mRNA), long non-coding RNA (lncRNA), microRNA (miRNA), and mitochondrial DNA/RNA (mtDNA and mtRNA), that serve as prospective biomarkers in assessment of various clinical conditions. For, e.g., free fetal DNA and RNA migrate into the maternal plasma, whereas circulating tumor DNA (ctDNA) has clinical relevance in diagnostic, prognostic, therapeutic targeting, and disease progression monitoring to improve precision medicine in cancer. The epigenetic modifications of ccfDNA as well as circulating cell-free RNA (ccfRNA) such as miRNA and lncRNA show disease-related variations and hold potential as epigenetic biomarkers. The messenger RNA present in the circulation or the circulating cell free mRNA (ccf-mRNA) and long non-coding RNA (ccf-lncRNA) have gradually become substantial in liquid biopsy by acting as effective biomarkers to assess various aspects of disease diagnosis and prognosis. Conversely, the simultaneous characterization of coding and non-coding RNAs in human biofluids still poses a significant hurdle. Moreover, a comprehensive assessment of ccfRNA that may reflect the tumor microenvironment is being explored. In this review, we focus on the novel approaches for exploring ccfDNA and ccfRNAs, specifically ccf-mRNA as biomarkers in clinical diagnosis and prognosis of cancer. Integrating the detection of circulating tumor DNA (ctDNA) for cancer genotyping in conjunction with ccfRNA both quantitatively and qualitatively, may potentially hold immense promise towards precision medicine. The current challenges and future directions in deciphering the complexity of cancer networks based on the dynamic state of ccfNAs will be discussed.