Cancer is a leading cause of high mortality rates around the world. Many scientists have explored anticancer peptides (ACPs), which are peptides with anti-tumor activity that can be safer than conventional drugs due to high activity coupled with high selectivity and delivery control. However, current in vitro methods of discovery are both time-consuming and expensive. This study aims to use modern machine learning tools to discover new ACP candidates.