Introduction: The airway surface liquid (ASL) is a thin fluid layer lining the conducting airways and consists of a low-viscosity periciliary liquid (PCL) layer and an overlying viscoelastic mucus layer that traps inhaled particles. Coordinated beating of motile cilia transports the ASL and entrapped material toward the upper airways via mucociliary clearance (MCC). This study presents a three-dimensional computational framework to investigate the fluid dynamics of MCC in the human trachea under physiologically realistic conditions, incorporating experimentally derived ciliary kinematics and mucus rheology. Methods: A hybrid immersed boundary–finite-difference projection method is employed to simulate the unsteady fluid dynamics of the ASL (Figure 1A). The immersed boundary framework allows for an accurate representation of cilia-driven fluid–structure interactions. Physiologically realistic ciliary properties are obtained from primary nasal epithelial cells derived from a healthy donor and cultured at the air-liquid interface. Ciliary beat patterns are extracted from high-resolution microscopy images and incorporated into the computational model (Figure 1B). The mucus layer is modeled as a nonlinear viscoelastic fluid using a five-mode Giesekus constitutive model. Differential dynamic microscopy is applied to quantify the frequency-dependent microrheological properties of mucus produced by epithelial cell cultures, providing experimentally derived inputs for the mucus rheology (Figure 1C and D). Results: The numerical simulations yield time-resolved, three-dimensional velocity fields of ASL transport in the human trachea. This study provides, for the first time, a physiologically realistic numerical framework that simultaneously incorporates experimentally measured ciliary beat patterns obtained from high-resolution microscopy and mucus viscoelastic properties derived from differential dynamic microscopy. The results offer detailed insight into the spatiotemporal dynamics of ASL flow and enable assessment of the sensitivity of MCC to key biophysical parameters. Furthermore, the developed framework has potential applications in optimizing MCC and improving the understanding of drug delivery processes within the ASL of healthy airways.