Mild Cognitive Impairment (MCI) is a risk of cognitive decline, commonly referred to as a transitional stage between normal cognition and dementia. Patients with MCI typically progress to Alzheimer's disease (AD), which causes cognitive deficits such as deterioration of their thinking abilities. This study aims to detect MCI patients using electroencephalography (EEG) signals. The EEG dataset used in this study consists of EEG signals recorded from 18 MCI and 16 control groups. Firstly, EEG signals were denoised using multiscale principal component analysis (multiscale PCA). Then, 36 features were extracted from the EEG signals using the discrete wavelet transform leader (DWT leader) feature extraction method. Finally, using the extracted feature vectors, control groups, and MCI groups were classified by ensemble learning algorithms. As a result, AdaBoostM1 algorithm has the highest success with 93.50% accuracy, 93.27% sensitivity, 93.75% specificity, 94.38% precision, 93.82% f1-score, and 86.97% Matthews correlation coefficient (MCC). By achieving quite satisfactory accuracy, this study proves that the ensemble learning algorithm can also be used for MCI detection.