As a climacteric fruit, apple fruit quality during storage is influenced by the activity of two browning-related enzymes, polyphenol oxidase (PPO) and peroxidase (POD). Therefore, to evaluate the enzymatic activity of Red Delicious apples, the content of PPO and POD was measured using destructive chemical methods and used as the response for visible/near-infrared (Vis/NIR) spectroscopy. Different variable selection algorithms were implemented in combination with two machine learning algorithms of support vector machine (SVM) and decision tree (DT), to identify the effective wavelengths from the whole spectral data. DT-FOA (forest optimization algorithm) algorithm outperformed other methods in terms of minimum number of effective wavelengths (EWs), minimum execution time, and maximum correlation. Multiple linear regression (MLR), partial least squares regression (PLSR), and artificial neural network (ANN) were applied to predict enzymatic activities. The selection of the optimum predictive model was mainly based on criteria such as the coefficient of determination (R2), root mean square error (RMSE), the ratio of prediction to deviation (RPD) of the validation set. ANN outperformed the MLR and PLSR in terms of the highest R2 (0.96 and 0.99) and RPD (4.87 and 6.96) in test phase of DT-FOA, for PPO and POD, respectively. However, all the model gave reliable results being the R2 above 0.92 and 0.93, and RPD above 5.36 and 5.31 for MLR and PLSR in test phase of DT-FOA, for PPO and POD respectively. The combination of Vis/NIR spectroscopy, regression algorithm and variable selection led to a tool for evaluating Red Delicious apple fruit.