Epilepsy is a neurological disorder that affects millions of individuals worldwide. The recurrent seizures associated with epilepsy significantly affect the quality of life of these patients. Accurate and timely seizure prediction will improve epilepsy management and enhance these patients' quality of life. In this thesis, a novel non patient-specific seizure prediction system is proposed using the graded spiking neural networks (GSNNs) implementable on Intel's Loihi 2 neuromorphic processor. Trained using the widely used CHB-MIT EEG dataset, the GSNN-based EEG seizure predictor achieved a very competitive performance, compared to existing EEG seizure prediction methods. To further enhance the quality of the proposed seizure predictor, The following tasks are performed: 1) optimized the predictor's hyperparameters, 2) reduced the amount of processed EEG data by selecting the optimal subset of EEG channels, and 3) incorporated a time-windowed voting mechanism to enhance the system's robustness to noise and artifacts. Finally the seizure prediction system is implemented on Intel's Loihi 2 neuromorphic processor and analyzed its performance. A comparison between the proposed system and those based on traditional artificial neural networks (ANNs) highlighted the potential of GSNN-based system for greater efficiency and lower computational complexity.