Erythrophagocytosis is a complex multiphysics process involving recognizing, engulfing, and digesting aged or diseased red blood cells (RBCs) by phagocytic cells. Biochemical signaling pathways mediated by ligand-receptor engagement have been considered as key factors in initiating and driving the phagocytosis of abnormal RBCs by tissue-resident macrophages in the spleen and the liver. However, growing evidence has underscored the effect of the stiffness of RBCs in modulating the engulfment process. Building on this evidence, the project proposes that erythrophagocytosis is not only governed by the biochemical signaling pathways but is also significantly impacted by the mechanics of RBCs. To validate the hypothesis and address the key question of how multiple biochemical signaling pathways and RBC biomechanics are intertwined in dictating the erythrophagocytosis, the project will develop an artificial intelligence (AI)-enhanced multiphysics and multiscale framework validated using multimodal experimental data. The project will apply this framework to quantify the impact of signaling pathways and RBC stiffness on macrophage-mediated RBC engulfment. The proposed framework is transformative to investigate the pathogenesis of various hemolytic anemia and the mechanisms of macrophage-based approaches for cancer immunotherapy. Integration of biochemical and biomechanical modeling using AI approaches bridges the gap between the spatial and temporal scales of molecular and cellular i