<p>The current study investigates the variations in heat transfer-based thermophysical characteristics that have significant impact on blood flow dynamics by the incorporation of hybrid nanomaterials with the help of Adam–Bashforth–Moulton informatics-driven Bayesian regularized neural networks (ABM-BR-NNs). The characteristics of a blood-based tri-hybrid nanofluid are analyzed using a tangent hyperbolic fluid model in a complex sinuous medium bounded by vertical walls, where the heat transport properties are further enhanced by the interaction of Hall current, Joule heating, and thermal radiation. Physical properties are used to describe the model for interacting alloy nanoparticles AA7072 and AA7075 with zirconium oxide ZrO₂ in base liquid blood. Similarity transformations are used to convert the designed model into a non-dimensional form. The primary objectives of the present study are to investigate recent advancements in nanofluid-based peristaltic transport relevant to biomedical applications, particularly within complex sinuous channels bounded by vertical walls, and to evaluate the potential of employing machine learning to enhance the performance of blood-based nanofluid suspension. The effectiveness of the machine learning procedure ABM-BR-NNs is evaluated using data produced by the Adam–Bashforth–Moulton numerical scheme. The dataset is partitioned into training, testing, and validation subsets in proportions of 80 %, 15 %, and 5 %, respectively, with model optimization carried out using the Bayesian regularization approach. The robustness of proposed methodology is demonstrated through regression analysis, mean squared error calculations, and error histogram. Heat transfer is enhanced, and flow behavior is regulated through the influence of MHD effects. However, the viscosity, heat transport, and stability characteristics are significantly affected by parameters such as the Hall parameter, power law index, Weissenberg number, and radiation parameter.</p>