The article reviews the development and application of pseudo amino acid composition (PseAAC) in predicting protein attributes. It discusses the construction of benchmark datasets, protein sample representation using PseAAC in different modes (functional domain, gene ontology, and sequential evolution), prediction algorithms (KNN classifier), and cross-validation tests. The review emphasizes the importance of PseAAC in capturing essential features of protein sequences and its wide range of applications in predicting various protein attributes, such as subcellular localization, enzyme function, and membrane protein types. The article also highlights the need for a user-friendly web server to make these prediction methods accessible to a broader scientific community.The article reviews the development and application of pseudo amino acid composition (PseAAC) in predicting protein attributes. It discusses the construction of benchmark datasets, protein sample representation using PseAAC in different modes (functional domain, gene ontology, and sequential evolution), prediction algorithms (KNN classifier), and cross-validation tests. The review emphasizes the importance of PseAAC in capturing essential features of protein sequences and its wide range of applications in predicting various protein attributes, such as subcellular localization, enzyme function, and membrane protein types. The article also highlights the need for a user-friendly web server to make these prediction methods accessible to a broader scientific community.