Rapid and non-destructive method for online determination of soluble solids content (SSC) in Shine Muscat grapes was developed by using visible-near infrared (Vis-NIR) spectroscopy. The influences of spectral acquisition speed, sampling mode, and preprocessing methods on model performance were systematically investigated, and model simplification was achieved while maintaining prediction accuracy. During spectral acquisition, each grape bunch was divided into three equal segments (front, middle, and rear), and the averaged spectra were used to represent the overall optical response of the whole bunch. Partial least squares regression (PLSR) and support vector regression (SVR) models were constructed to evaluate the effects of different preprocessing approaches. Among them, the combination of Savitzky-Golay smoothing and standard normal variate transformation (SG+SNV) yielded the best results by effectively correcting baseline drift and scattering noise, thereby enhancing spectral quality and model precision. Whole-bunch detection achieved superior performance compared with segmented detection, with SVR producing root mean square error of prediction of 0.49°Brix and correlation coefficient of 0.91, and PLSR yielding root mean square error of prediction of 0.45°Brix and correlation coefficient of 0.94. As the acquisition speed was increased from 0.15m/s to 0.6m/s, the prediction accuracy was gradually declined. For wavelength selection, uninformative variable elimination (UVE) demonstrated the best performance, extracting 314 informative wavelengths from 980 while maintaining high accuracy (PLSR: root mean square error of prediction was 0.41°Brix, correlation coefficient was 0.90) and reducing model complexity. Overall, the proposed Vis-NIR-based approach enabled accurate and stable online prediction of grape SSC at the whole-bunch level, offering a practical theoretical foundation for the commercial development of intelligent online sorting systems for bunch-type fruits.