The use of satellite imagery in remote sensing is increasingly important and has wide applications, including land cover and land use classification, environmental monitoring, and disaster management. Sentinel-2 satellites produce multispectral images that capture data across thirteen spectral bands, from visible and near-infrared to shortwave infrared. Each band captures specific information about the Earth’s surface. Machine learning algorithms play a vital role in the classification process of land cover and land use classification process. One of the most important factors affecting classification accuracy, which is considered an influential problem, is the choice of the number of bands involved in the classification process. In general, increasing the number of bands can provide more helpful information in the classification process, but on the other hand, increasing the number of bands can lead to noise or redundancy, thus reducing the classification accuracy of the machine learning algorithm. The present study aims to study the effect of the number of bands by conducting several experiments, comparing them, and finding their effect on classification accuracy. Three types of classifiers were used in this article: random forest (RF), classification and regression trees (CART), and K-nearest neighbor (KNN). This article includes several steps, the most important of which are choosing the region of interest (ROI) (Karbala in Iraq) and the time series. This study analyzes three classification techniques and the effect of the number of spectral bands on their performance. By conducting three experiments, the study concluded that the contribution of spectral bands greatly affects the classification accuracy, as the bands were chosen according to the accuracy. Four bands with an accuracy of 10 m in the first experiment, six bands with an accuracy of 20 m in the second experiment, and in the last experiment, ten bands were taken. The experiment of four bands with an accuracy of 10 m gave the highest accuracy in the three classifiers, as it was (Overall accuracy was 95%, 90%, and 90% for RF, CART, and KNN, respectively, and Kappa coefficient was 93%, 87%, and 88% for RF, CART, and KNN respectively).