Artificial intelligence-based diagnostic systems are increasingly vital in clinical decision support. This study introduces TiMoS-Net, a novel multi-temporal image and spatial morphological feature fusion network designed for early prediction of neoadjuvant chemotherapy (NAC) response in breast cancer. TiMoS-Net integrates longitudinal MRI data with advanced spatial-morphological features, leveraging a novel a pre-training strategy combining self-supervision and label guidance, a temporal attention mechanism to capture dynamic tumor changes, and sophisticated topological and fractal morphological feature extraction, followed by a genetic algorithm-based feature fusion. Validated across multiple patient cohorts totaling 452 individuals, the model demonstrated superior predictive performance for pathological complete response (pCR), achieving areas under the curve (AUCs) of 0.898 and 0.922 on the external validation cohorts, significantly outperforming conventional single-timepoint approaches and models relying solely on temporal image data. Ablation studies confirmed the significant contributions of both the temporal attention mechanism and the advanced morphological features to the model’s efficacy. Furthermore, radiogenomic analysis linked the image-derived predictions to distinct biological pathways and immune cell infiltration patterns, and survival analysis indicated a significant association between the predicted pCR status and improved patient outcomes. TiMoS-Net offers a robust and interpretable approach for enhancing NAC response prediction, providing valuable insights for personalized treatment strategies in breast cancer.