Vine fertilization is a tool that allows winegrowers to influence and regulate the quality of their wine. Today, nutritional analysis is done by using a CHNS analyzer and mass spectroscopy. However, these methods are destructive and time consuming. Another approach is to use near-infrared (NIR) spectroscopy, which, when coupled with chemometric tools, allows users to develop prediction models. This approach is widely used today in agriculture. In this study, we focus on the relative amount of carbon (C), hydrogen (H), nitrogen (N), and sulfur (S), in dry matter (DM), and on the C:N ratio. The relative amount of these elements was obtained by applying NIR spectroscopy to 252 samples of various fresh and dried vine organs. Each partial least squares model was tested on an external prediction set. The coefficient of determination for prediction (r(2)), the root-mean-square error of prediction (RMSEP), and the ratio of performance of prediction (RPD) were obtained for C (0.49, 2.24% of DM, and 1.33 for fresh material with MSC; 0.45, 2.37% of DM, and 1.26 for dry material with MSC, respectively), H (0.56, 0.27% of DM, and 1.45 for fresh material with D1; 0.49, 0.30% of DM, and 1.32 for dry material with D1, respectively), N (0.91, 0.17% of DM, and 3.32 for fresh material with raw spectra; 0.95, 0.13% of DM, and 4.39 for dry material with MSC, respectively), S (0.47, 0.046% of DM, and 1.31 for fresh material with MSC; 0.46, 0.046% of DM, and 1.30 for dry material with D2, respectively), and the C:N ratio (0.85, 8.20, and 2.58 for raw spectra of fresh material; 0.87, 7.55, and 2.80 for dry material with D2, respectively). These results show that NIR spectroscopy can be used to assess the status of nitrogen nutrition in vines and to monitor the C:N ratio.