This study leverages the DCTPep database, a comprehensive repository of cancer therapy peptides, to explore the application of machine learning in accelerating cancer research. We applied Principal Component Analysis (PCA) and K-means clustering to categorize cancer therapy peptides based on their physicochemical properties. Our analysis identified three distinct clusters, each characterized by unique features such as sequence length, isoelectric point (pI), net charge, and mass. These findings provide valuable insights into the key properties that influence peptide efficacy, offering a foundation for the design of new therapeutic peptides. Future work will focus on experimental validation and the integration of additional data sources to refine the clustering and enhance the predictive power of the model, ultimately contributing to the development of more effective peptide-based cancer treatments.