The Penaeus Vannamei (Pacific white shrimp), a commercially significant aquaculture species with considerable economic value in global markets, exhibits high susceptibility to quality deterioration, which necessitates the development of rapid and accurate techniques for freshness evaluation. In this study, we established an innovative analytical approach based on near-infrared (NIR) spectroscopy coupled with comprehensive chemometric analysis to overcome the limitations of traditional time-consuming physicochemical detection methods. Specifically, we systematically acquired NIR spectral data from shrimp samples while concurrently monitoring multiple physicochemical indicators, including total volatile basic nitrogen (TVB-N) and texture parameters, throughout the deterioration process to elucidate their temporal evolution patterns and correlations with freshness degradation. The acquired spectral data underwent sophisticated preprocessing using Savitzky-Golay first derivative transformation, followed by wavelength optimization through the successive projections algorithm (SPA), which effectively identified characteristic spectral bands within the 680–1400 nm region corresponding to specific quality parameters. Based on these optimized spectral features, we developed and validated two complementary chemometric models: an SPA-optimized support vector machine (SVM) model that achieved a robust coefficient of determination (R2 = 0.76) for TVB-N prediction, and a full-spectrum Levenberg–Marquardt neural network model that demonstrated comparable predictive performance with an R2 of 0.73. Furthermore, recognizing the inherent limitations of individual physicochemical indicators in providing comprehensive freshness assessment, we developed a novel composite freshness index through multivariate evaluation, which enabled the establishment of a scientifically rigorous four-tier classification system for Penaeus Vannamei freshness: Grade I (excellent), Grade II (edible), Grade III (not recommended), and Grade IV (inedible). The superior predictive capabilities and practical applicability of our proposed methodology demonstrate its significant potential for rapid and reliable freshness evaluation in commercial shrimp processing and quality control applications.