Deep learning has improved subject-specific epileptic seizure detection from EEG signals, but cross-subject performance remains challenging due to inter-subject variability like age, seizure types, and gender. To tackle this challenge, we present a Multi-Scale Spatiotemporal Deep Convolutional Network (MSSTDCN) enhanced with Power Spectral Density (PSD) feature extraction for improved generalization. The framework includes signal preprocessing, data augmentation, and class balancing to boost robustness. PSD features from multiple frequency bands are integrated and classified to distinguish seizures from interictal states. Experiments show that MSSTDCN achieves 87.07% accuracy on the CHB-MIT dataset, outperforming baselines by 2.39%, and 88.05% on the Siena dataset, with a 2.36% improvement. These results highlight the model’s strong cross-subject generalization and potential for clinical epilepsy diagnosis.