Dyslexia is a learning disability characterised by a lack of reading abilities and difficulties naming words quickly and correctly. Dyslexic people struggle with the reading and comprehending of words or letters. Hence, this research presents a methodology for classifying dyslexic and normal children using rest electroencephalogram (EEG) signals. The EEG signals are collected from 19 different channels located in the occipital, temporal, frontal, and parietal regions of the brain of twenty children with dyslexia and sixteen normal youngsters (ages 8 to 16 years). Data segmentation is used in the captured signal which is at rest for 2 min, 10-s-long sample is extracted, resulting in a total of 12 samples for one participant. Data balancing is applied to improve the accuracy of the machine learning models. A total of fourteen features from each channel are collected and fed into the classifier. The prediction model employs support vector machine (SVM) learnings such as linear, quadratic, cubic, and median Gaussian. The preprocessed raw EEG signal extracts band separated features using the discrete wavelet transform (DWT). In total, 266 features were extracted. The ensemble-based feature selection method is used to determine the most significant features, reducing the computational pressure on classifiers. Experimental results show that the most significant features derived by the gradient boost technique combined with the SVM classifier produced the maximum classification accuracy of 97.2%, which is the best performance among the studies used for comparison. Computer-aided diagnosis tools for early dyslexia detection could be developed with the help of effective and automatic categorisation using EEG signals.