摘要Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath.How-ever,the accurate prediction and control of printability remain fundamental challenges due to the complex interactions be-tween inks and support baths.Here,we present an artificial intelligence(AI)-driven framework that interprets and predicts embedded printability using rheological data.Using a standardized workflow,we extracted 21 rheological descriptors and es-tablished 12 indicators to evaluate structural continuity and geometric fidelity.Interpretable machine learning models re-vealed that direction-dependent defects are governed by the synergistic interplay among ink yield stress,support bath zero-shear viscosity,flow behavior index,and time constant.To enable the prediction of printability in a generalizable manner,we further developed a cascaded neural network,which achieved mean relative prediction errors below 15%across all indica-tors.Experimental validation using three-dimensional(3D)-printed constructs and micro-computed tomography(μCT)recon-structions confirmed a strong correlation between predicted and actual fidelity.This work establishes a physics-informed,data-driven paradigm for decoding and optimizing embedded printing,offering broad applicability and providing a robust tool for the rapid pairing of suitable printable ink-support bath combinations.