Epilepsy is a prevalent chronic neurological disorder, and the unpredictability of seizures significantly impacts patients’ quality of life. Electroencephalography (EEG) signals hold considerable potential for seizure prediction. Existing methods focus on either short-term or long-term EEG features, neglecting their integration and limiting seizure prediction efficacy. In this study, we propose a novel deep learning architecture, the WTConv-Parallelised Attention Network (WTPAN-Net), which integrates two key modules: the Wavelet Convolution (WTConv) module for capturing multi-scale features in the wavelet transform domain through dilated receptive fields, addressing the non-smooth nature of EEG signals, and the Parallelised Attention (PPA) module for dynamically fusing local and global features through a parallelised attention mechanism, enhancing the model’s sensitivity to key pre-seizure patterns. Experiments on the CHB-MIT dataset demonstrate WTPAN-Net’s effectiveness, achieving accuracy, sensitivity, and specificity levels above 95%. Ablation studies confirm the critical roles of both modules, and comparisons with state-of-the-art models show WTPAN-Net’s superior predictive performance, offering a reliable solution for epilepsy seizure prediction.