The thick peel and large volume of pomelos result in low spectral signal intensity, leading to poor performance in nondestructive internal quality detection. This study proposes a Monte Carlo-based simulation and experimental verification for the full-transmission light transport and nondestructive internal quality detection of pomelos with multiple tissue layers. By measuring the optical properties of the multiple tissue layers of pomelos, we simulated the photon travel distance and interaction probabilities within these layers. The light transport results were obtained after simulating the photon interactions with the multilayer tissues. Following this, modeling simulations were conducted based on the optical properties of these layers. We varied the incident angle of the light source and the rotation angle of the detector to find the optimal angles for light transmission. Finally, an experimental platform was constructed to verify the findings. The near-infrared spectral data underwent preprocessing steps such as Savitzky-Golay (SG) smoothing, standard normal variate (SNV) transformation, and competitive adaptive reweighted sampling (CARS) for feature extraction, followed by partial least squares regression (PLSR) modeling. The study results indicated that photons traveled the longest distance and experienced the greatest attenuation with the lowest survival probability in the epicarp oil cell layer, whereas photons traveled the shortest distance and had the highest survival probability in the pulp lobes. The optimal parameters for light transmission were a light source incident angle of 36° and a detector rotation angle of 10° around the Z-axis. The modeling results with these optimal parameters showed R² and RMSEC values of 0.89 and 0.25 for the training set, and R² and RMSEP values of 0.84 and 0.38 for the prediction set. In contrast, without parameter optimization, the results showed a light source incident angle of 0° and a detector rotation of 0°, with the training set R² and RMSEC being 0.85 and 0.27, and the prediction set R² and RMSEP being 0.80 and 0.34. For a light source incident angle of 18° and a detector rotation of 10° around the Z-axis, the training set R² and RMSEC were 0.80 and 0.34, while the prediction set R² and RMSEP were 0.73 and 0.74. For a light source incident angle of 36° and a detector rotation of 10° around the Y-axis, the training set R² and RMSEC were 0.69 and 0.25, while the prediction set R² and RMSEP were 0.60 and 0.83. The findings of this study on the light transport parameters for multiple tissue layers of pomelos can improve the effectiveness of nondestructive internal quality detection. Additionally, these results provide a reference for the simulation and experimental nondestructive internal quality detection of other multi-tissue layer fruits using full-transmission light transport.