<p>This research deals with the optimization of the nonlinear thermal flow of a viscous fuzzy ternary hybrid nanofluid, driving insights for biomedical applications. A ternary hybrid nanofluid formed using blood as the host fluid, integrated with platelet-shaped zinc (Zn), spherical ferric oxide (Fe₃O₄), and cylindrical gold (Au) nanoparticles. The study aims to precisely determine the best nanoparticle volume to enhance thermal conductivity and streamline fluid dynamics, taking into account the inherent ambiguities. The governing equations of the flow are converted into fuzzy differential equations, solved carefully via the finite difference method (FDM) to produce comprehensive numerical data. The present study accounts the triangular fuzzy numbers (TFNs) to scrutinize the influence of the fuzzy volume fraction of nanoparticles on the composite nanofluid flow, effectively accounting for uncertainties. This approach enables a solid comparison of the thermal profiles of simple and hybrid nanofluids, disclosing the greater heat transfer efficiency of hybrid systems. The numerical data of engineering parameters is then processed by an Artificial Neural Network (ANN) to predict the hydrodynamic and thermal performance Indicators. This involves a comparative application of both the Levenberg–Marquardt (LM) and Bayesian Regularization (BR) algorithms. The outstanding overlap between projected and targeted values demonstrates the power of strong ANN framework, offering a foundation for better decision-making in nanofluid concentration and optimization for applications in antiviral therapies, cancer treatment, and targeted drug delivery systems.</p>