Cancer as a disease caused by pathological changes in cellular division, has become a leading cause of death worldwide. The persistent prevalence of cancer worldwide results in the loss of millions of lives annually. Traditional cancer treatment methods often inflict significant harm on patients. However, Anticancer peptides (ACPs), a class of small peptides offer several advantages including high specificity, low immunogen-icity, minimal toxicity, and high tolerance under normal physiological conditions. It provides a potential alternative for cancer treatment. Traditional laboratory methods for identifying these peptides are time-consuming, expensive, and inefficient. In contrast, machine learning methods can be used to predict anticancer peptides, requiring only computational resources. This approach offers a more efficient and cost-effective means of identifying potential can-didates for anticancer therapy.

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