Abstract: Anticancer peptides (ACPs) have become a focal point of research due to their high efficacy, low toxicity, and high selectivity. Methods of ACP identification and design based on artificial intelligence (AI) surpass traditional experimental techniques in cost-efficiency, success rate, and the ability to investigate a broader sequence space. This article highlights the application of AI technology in the generation and identification of ACPs, including the exploration of new ACP design through deep generative models and ACP identification methods based on machine learning and deep learning. Furthermore, it discusses challenges in current research, such as insufficient model reproducibility and interpretability, and a lack of experimentally validated negative data. Future research directions are proposed to provide new insights for the development of anticancer peptides, aiming to enhance the understanding and development of ACPs.