N-linked glycosylation is crucial for protein function and stability, making accurate prediction of glycosylation sites essential for understanding key biological processes. Traditional prediction models often focus on limited sequence windows and seldom consider entire protein sequences. These models lack the ability to capture complex structural and contextual information essential for accurate site prediction, overlooking critical interactions across the protein that influence glycosylation. We introduce SGGly, a highly accurate N-linked glycosylation prediction tool that leverages advancements in protein modeling, integrating full protein sequence embeddings from the transformer-based ProtBERT language model with structural features such as solvent accessibility and spatial degrees of freedom around asparagine residues. Using a novel whole-sequence similarity calculation, we developed a low-redundancy test dataset based on experimentally verified UniProt data, achieving high predictive accuracy. Additionally, in a comparative study with the N-GlyDE independent dataset, SGGly outperformed existing tools, demonstrating superior specificity, sensitivity, accuracy, and AUC. An ablation study confirmed the critical role of structural features in boosting predictive accuracy, while further fine-tuning enhanced recall and sensitivity, underscoring the model’s robustness in identifying true glycosylation sites.