Protein S-nitrosylation (SNO) is a process involving the covalent modification of cysteine residues by nitric oxide (NO) and its derivatives. Numerous studies have demonstrated that SNO is significantly involved in cell function and pathophysiology. The identification of SNO sites is significant in clarifying their function in cellular physiology, disease processes, and potential treatment strategies, rendering it of paramount importance in medical science. This study developed a machine learning (ML) model named “K-SNOpred” and found notable performance in identifying SNO sites using the Latent Semantic Analysis (LSA) feature embedding system. After collecting dbSNO and RecSNO datasets from the literature search, we applied three feature embedding systems: Doc2vec, FastText, and LSA on each dataset. The study employed various ML models and assessed their performance using multiple evaluation metrics through independent testing and 10-fold cross-validation. The evaluation's outcomes demonstrate that the proposed model achieved an accuracy of 87.56 % and an AUC score of 95.06 %, outperforming existing state-of-the-art (SOTA) models by nearly 10 % in accuracy and 6 % in AUC. Furthermore, the model demonstrated balanced sensitivity and specificity, indicating its ability to detect both positive and negative SNO sites accurately. The outstanding performance of the K-SNOpred model demonstrates its high potential for clinical use and its applicability in the biotechnology field.