The generation of high-resolution boreal peatland maps through the integration of multi-source remote sensing data provides critical support for greenhouse gas inventories and environmental management. In this study, we propose GeoFusion, a pixel-wise peatland classification architecture that leverages open-access radar imagery from Sentinel-1 and optical imagery from Sentinel-2. GeoFusion combines convolutional neural networks (CNNs) for hierarchical local feature extraction with vision transformers (ViTs) for modeling long-range dependencies, enabling effective fusion of spectral–textural and structural information. The architecture also incorporates data augmentation, multi-head self-attention, and dropout regularization to enhance robustness and mitigate overfitting. Performance was assessed at two classification levels: site type and fertility level, capturing both ecological diversity and functional characteristics of complex peatland ecosystems. Comparative experiments against well-known deep learning models and hybrid Transformer-based models were conducted across three ecologically distinct regions in Finland. The obtained results demonstrate that GeoFusion outperforms other models and achieves average accuracies of 79.25% and 72.68% for fertility-level and site-type predictions, respectively.