Predicting epilepsy ahead of its occurrence has been an arduous job for scientists for a long time. Epileptic patients are still endeavoring to find a prosperous way to evade seizures to improve the quality of their lives. In this paper, we propose a novel deep learning system for epileptic seizure prediction using multi-channel electroencephalogram (EEG) recordings from the scalp of human brains. The proposed system is patient-specific and is predicated on the classification between the interictal and preictal brain states for the epileptic patient. The system uses a two-dimensional convolutional variational autoencoder and trains it once in a supervised way for automatic feature learning and classification. Within a prediction window of up to one hour, our proposed system achieved an average sensitivity of 94.45% and 0.06FP/h average false prediction rate which makes it one of the most efficient among state-of-the-art methods.