<p>Biological fluids exhibit complex rheological behaviors that are essential for physiological function and significantly altered in disease. Direct studies of native fluids, however, are constrained by invasive sampling, variability, and limited reproducibility, underscoring the importance of developing mimicking fluids. Unlike early substitutes based on Newtonian solutions, next-generation mimicking systems aim to reproduce non-Newtonian features such as thixotropy, and pH-dependent transitions. Recent advances highlight multicomponent formulations and simulation frameworks that more faithfully capture the defining rheological signatures of biofluids, yet most approaches remain preliminary and rely on simplified or animal-derived components. This review synthesizes progress in mimicking systems for major biofluids, identifies current challenges including limited pathological data and lack of standardized protocols, and emphasizes the need for integrated experimental and computational strategies. Looking ahead, future efforts should combine microstructural characterization, standardized rheological databases, and AI-enhanced modeling with sustainable materials. Such developments will establish physiologically accurate and reproducible mimicking fluids, enabling broad applications in diagnostics, medical device testing, and precision medicine.</p> Graphical abstract <p></p>