The development of systems that can predict epileptic seizures in real-time offers great hope for epilepsy patients. These systems aim to prevent accidents that patients may experience caused by the loss of consciousness during seizures. Therefore, patients must use real-time epileptic seizure prediction systems that do not interfere with their daily activities. In this study, using the unipolar EEG data from a surface electrode, a patient-specific estimation system is implemented in real-time on a system on chip (SoC) that contains an embedded processor and programmable logic blocks. The European epilepsy database EPILEPSIAE is used in the scope of this work. In the proposed system, pre-processing is applied to the EEG data. Then, the features of the data in the frequency domain are extracted. The classifier model is trained with the RusBoosted Tree cluster classifier, which is a machine learning algorithm. Testing is carried out using the proposed classification model. Threshold values are determined, and then false alarms and erroneous classifications are prevented by post-processing. At the end of the tests, prediction success, sensitivity (SEN), Specificity (SPE), False Prediction Rate (FPR), and prediction times are obtained as 77.30%, 95.94%, 0.041 h_1, and 33.23 min, respectively. The proposed system outperforms other studies in the liter-ature in the number of electrodes, real-time operation, hardware/software architecture, and FPR performance. A wearable seizure prediction system seems to be commercialized according to the results achieved in this study.