This study explores the potential application of NIR spectroscopy coupled with different linear and nonlinear models for rapid evaluation of n-alkanes in crude oil. Samples for calibration were 30 model mixtures of n-eicosane in crude oil samples with a concentration of 1–15%. The prediction models were established based on 21 methods: linear regression, regression trees, support vector machines, Gaussian process regression, ensembles of trees, and neural networks. The spectral range 4500–9000 cm⁻¹ was determined to be the most informative for prediction. The prediction capability of lineal regression methods turned out to be unsatisfactory. Nonlinear models were preferred over linear models; better results were obtained using the regression trees method, including «fine tree» (RMSE = 2.8635) and neural networks (RMSE = 2.0157). The LS-SVM model exhibited satisfactory prediction performance (R² = 0.96, RMSE = 0.91), as did the Gaussian Process Regression Matern 5.2 GPR (R² = 0.96, RMSE = 1.03) and Gaussian Process Regression (Rational Quadratic) (R² = 0.95, RMSE = 1.04). Among the 21 chemometric algorithms, the best and weakest models were the LS-SVM and PLSR models, respectively. The LS-SVM model was the optimal model for the prediction of n-alkanes content in crude oil.