Multiple Sclerosis (MS) is a chronic and autoimmune neurological disease that is frequently seen especially in young people. MS lesions that can be seen with magnetic resonance imaging (MRI) findings are important biomarkers that provide information about the clinical prognosis and activity of the disease. The presence of new MS lesions is associated with future disease activity. This study aims to predict the future activity of MS using the 3D discrete wavelet transform (DWT) as a feature extraction method from 3D MRI. The 3D-DWT can be used as it provides spatial and spectral location features of MS lesions without losing their relationship between MRI slices. Ten different wavelet families of DWT are used individually, each of them is classified by six machine learning algorithms, and their feature extraction performances are compared. The highest F1-score, Precision, and Recall of 95.0% are obtained by the support vector machine algorithm on the SYM4, SYM8, and Haar wavelet families in the 3D MRI dataset consisting of 40 patients based on 5-fold cross validation. The results show that the 3D-DWT method is an effective method for feature extraction in predicting the future activity of MS.