Predawn leaf water potential (Ψ_pd), commonly accessed by using a Scholander type pressure chamber, is the main parameter to determine plant water status and it has been largely used to support irrigation management. However, this methodology is laborious, time-consuming and invasive, limiting its usage in mapping the water status of plants in large plantation areas. In this study, a low-cost hyperspectral proximal sensor to estimate Ψ_pd in grapevine (Vitis vinifera L.) was examined. For this, both Ψ_pd and spectral reflectance (340-850 nm) were accessed in grapevines in a commercial vineyard located at Douro Wine Region, northeast Portugal. A machine learning algorithm was tested and validated to assess grapevine water status. The experiment was performed in a randomized design with 12 grapevines (Touriga Nacional) per irrigation treatment, which were: non-irrigated, irrigation to replace 30% evapotranspired (Etc) water volume, and 60% Etc. The Ψ_pd and spectral data were determined weekly over six consecutive weeks, totaling 216 observations. The dataset was analyzed using Principal Component Analysis (PCA), and the machine learning regression algorithm applied was Random Forest (RF). Results from the validation dataset (n = 65 observations) for the RF tested exhibited a root mean square error (RMSE) of 0.25 MPa, mean absolute error (MAPE) of 29.55% and an R² value of 0.94. These results demonstrate that the hyperspectral sensor and RF algorithm can be accurately used to predict Ψ_pd in vineyards regardless of plant water status. This methodology emerges as a tool to assist vineyards producers in making decisions on irrigation management.