Meibomian glands possess intricate 3D architecture and uniquely rich innervation among sebaceous glands. Disruption of gland function and progressive morphological degeneration are central to meibomian gland dysfunction (MGD), the leading cause of evaporative dry eye disease (eDED), though the role of altered innervation to this pathogenesis remains unknown. However, quantifying these structures eyelid-wide is hindered by 2D histology and a challenging optical environment of absorptive melanin and highly scattering meibum (lipid-rich secretions). To overcome this, we developed a modified LIMPID clearing protocol for whole, intact eyelids. We achieved high-resolution, volumetric imaging of the entire gland bed using simultaneous light and peroxide to eliminate melanin, alongside dichloromethane to extract meibum. To bridge imaging and pathology, we optimized Hoechst 33342 and endogenous autofluorescence to generate recolored 3D virtual histology (3DVH). Uniquely, we multiplexed 3DVH (m3DVH) with immunofluorescence to map global innervation. We coupled this pipeline with nnU-Net-based digital labeling to segment MG structures and custom algorithms for precise 3D nerve segmentation. Applying this workflow to Awat2[-/-] knockout mice, eDED murine model, we quantified structural shifts at an unprecedented scale. While previous methods have demonstrated ductal dilation, our 3D analysis uniquely quantified significantly thicker duct diameters across the entire gland bed. As a pilot validation of our platform's utility in an MGD-relevant disease model, we applied m3DVH to Awat2[-/-] eyelids and detected reduced measurable TUJ1-positive nerve density, particularly within gland-associated regions, compared to wild-type controls. This approach enables precise, in situ structural mapping, establishing a quantitative framework for assessing DED pathogenesis and emerging therapeutics.