Near-infrared hyperspectral imaging (NIR-HSI) is widely used as a non-destructive technique for evaluating internal fruit quality; however, reliable pixel-wise visualization remains challenging due to geometry-induced spectral distortions and the lack of statistically interpretable validation criteria. This study proposes an integrated framework for three-dimensional visualization of soluble solids content (SSC) across the entire surface of strawberries using NIR-HSI combined with shape-aware spectral correction and pixel-level reliability assessment. Two complementary imaging systems—a line-scan system and a rotation-scan system—were used to acquire hyperspectral and 3D shape data. Fruit height and surface orientation were incorporated into spectral preprocessing to reduce illumination and curvature effects. Partial least squares regression (PLSR) models were developed using region-of-interest-averaged spectra and applied to pixel-wise SSC mapping. To assess the statistical validity of pixel-level predictions, an imaging reliability index based on the Mahalanobis distance in the PLS score space was introduced. The results show that models with high sample-level accuracy do not necessarily produce reliable SSC maps, whereas reliability-based model selection improves image interpretability. This framework enables consistent three-dimensional SSC visualization and is applicable to hyperspectral imaging of internal fruit attributes.