With the rapid growth of virtual agents and humanoid robots, effectively building user trust through embodied interactions has become a crucial research focus. However, current virtual agents typically lack structured feedback mechanisms, limiting their capability to elicit stable user trust and long-term engagement. To address this, we propose a multidimensional embodied feedback model structured around a clear “Input–Mediator–Output (IMO)” cognitive pathway, which systematically integrates six feedback variables across visual, auditory, and behavioral channels. The variables—Facial Expressiveness, Prosodic Warmth, Proactive Responsiveness, Movement Synchrony, Social-Linguistic Expressiveness, and Turn-Taking Coordination—were extracted from recent literature and operationalized into measurable cognitive pathways involving perceptual initiation, attributional interpretation, and behavioral intention. To facilitate empirical validation, six causal hypotheses (H1–H6) were defined, supported by detailed experimental manipulation conditions. Two verification pathways are further proposed: expert evaluation via Delphi methods and controlled behavioral experiments using interactive prototypes. Additionally, we conducted scenario adaptability analyses, comparing the implementation feasibility of these variables across physical robots, virtual avatars, and emotional companionship systems. This comparison was visually demonstrated through an Embodied Mapping Matrix, clarifying the differential priorities and applicability of each feedback channel according to system type. This work contributes a structured, empirically testable framework that enhances both theoretical understanding and practical implementation of trust-building in virtual and humanoid agents. The proposed model and validation methods offer valuable insights for designers to systematically optimize interaction modalities, thereby fostering robust user trust and promoting sustained engagement across diverse human–AI interaction contexts.