Protein S-palmitoylation is a critical and reversible lipid modification that governs protein localization, trafficking, and signaling. Its dysregulation is increasingly implicated in cancer and therapeutic resistance, highlighting an urgent need for high-throughput computational prediction tools. Palmitoylation is regulated by a complex interplay of sequence motifs, structural conformations, and physicochemical properties. To comprehensively capture these determinants, we developed Deep-Palm: a deep learning framework that integrates multi-view features, including amino acid sequences, spatial constraints from predicted structures, physicochemical descriptors, and protein language model embeddings, for accurate prediction of S-palmitoylation sites. In independent testing, Deep-Palm achieved an area under the curve (AUC) of 0.931, substantially outperforming state-of-the-art tools such as pCysMod, MusiteDeep, and GPS-Palm. Furthermore, Deep-Palm demonstrated robust performance across diverse eukaryotic species. Notably, its predictive accuracy remained consistent regardless of protein functional categories or subcellular localization, indicating that the model captures fundamental, context-invariant determinants of palmitoylation. By embedding amino acid sequences with structural and protein property awareness, Deep-Palm not only delivers stable and high-precision predictions but also provides a framework for uncovering novel regulatory mechanisms and therapeutic targets in protein post-translational modification (PTM).