The assessment of plant nutrient status to provide sufficient fertilization for rapid and continuous uptake by plants has been based on visual diagnosis in the field, which is quick but demands a lot of experience and has low operability. Visible near-infrared spectroscopy (VNIS) has shown to be a quick, non-destructive, accurate, and cost-effective analytical method in precision agriculture. In this study, we assessed the potential of this technology to predict phosphorus and potassium content in wheat leaves using different multivariate regression methods. The hyperspectral and reference measurements were taken from wheat plant leaves grown in a long-term fertilization trial under contrasted concentrations of phosphorus and potassium. The leaf proximal and hyperspectral data were collected using an ASD FieldSpec4 spectroradiometer operating in the spectral range from 350 to 2500 nm. Before conducting the analysis, the leaves spectra were preprocessed with a Savitzky–Golay smooth filter and a Standard Normal Variate normalization method. A total of 60 samples, collected between flowering and maturity stages, combined with the preprocessed spectra were used to develop support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN) prediction models for estimating leaves phosphorus content (LPC) and leaves potassium content (LKC). The entire sample set was randomly split into a training set (70%) and a test set (30%), and the performances of the different prediction models were compared using normalized root mean square error (NRMSE) and coefficient of determination (R2) in both cross-validation and testing processes. The results showed that LPC prediction models outperformed the LKC models, with high accuracies (R²) in cross-validation in the order of 0.83, 0.75, and 0.79 for SVR, KNN, and RF, respectively. For potassium, the coefficient of determination of cross-validation was 0.64, 0.51, and 0.55 for SVR, KNN, and RF, respectively. The highest validation results were returned by the RF algorithm for both LPC and LKC predictions, with moderate R² values equal to 0.56 and 0.53, respectively. In the RF model, phosphorus and potassium in wheat leaves can be predicted with errors of 19 and 13%, respectively.