CKD (chronic kidney disease) is recognized as a globally prevalent chronic disorder. Early diagnosis and in-depth elucidation of its pathological mechanisms are considered of great significance for improving patient outcomes. As an important branch of proteomics, peptidomics is employed to reveal the complex pathophysiological mechanisms and potential biomarkers of CKD through in-depth analysis of endogenous peptides in urine and plasma. Currently, peptidomics technology is shown to possess broad application potential in the etiological differentiation, disease staging, prognosis evaluation, and therapeutic response monitoring of CKD. In particular, significant progress is made in the construction of multi-etiology differential models by integrating machine learning approaches. In this review, the association between urinary peptides and key pathological processes of CKD, such as renal fibrosis and inflammation, was systematically summarized. The application value of peptidomics in precision medicine was thereby emphasized. By integrating the latest clinical and multi-omics data, the advantages and challenges of peptidomics in the clinical translation of CKD were discussed. This review aims to provide a theoretical basis and reference for the future application and development of peptidomics technology in the diagnosis and treatment of CKD.