Peptide drugs have broad application prospects in the medical field due to their strong selectivity, strong efficacy, good biocompatibility, and low toxicity. Peptide drugs can be used in the treatment of various diseases, such as oral diseases, cardiovascular diseases, diseases related to metabolic dysfunction, autoimmune, endocrine, skin, and bone diseases. Traditional peptide drug development methods are time-consuming and labor-intensive, it is difficult to cover every potential peptide sequence combination, and expensive tests are required to evaluate the efficacy of synthesized peptides. In recent years, artificial intelligence (AI) has developed rapidly, and many traditional machine learning and deep learning algorithms have been applied to the development of peptide drugs. AI-assisted peptide drug development employs peptide databases and various models to screen or generate therapeutic peptides in a short period of time. This chapter outlines the characteristics and clinical applications of peptides, the development of traditional peptide drug development, and exemplifies the specific applications of traditional machine learning and deep learning algorithms in peptide drug development.