The synaptic disturbance in the prefrontal portion of the brain induces epileptic seizures. Electroencephalography is a noninvasive tool for diagnosing the different brain disorders including the seizures. Visual inspection of these signals for prognosis of a disease is very time consuming. Therefore, automated epileptic seizure detection methods are adopted by the medical practitioners for fast analysis and accurate detection of disease. An integrated method was proposed in this work, in which wavelet packet decomposition (WPD) method is implemented for time frequency transformation and then the features are extracted. After feature extraction, four different classification models are compared using the balanced train test split method, where 70% train dataset and 30% test dataset have been taken for validation of the model. The results reveal that WPD with SVM has an accuracy of 96% which outperforms other conventional models applied to the benchmark dataset of Bonn University’s EEG signal for seizure prediction.