IL-2 is a small 15.5-kDa four α-helical bundle cytokine, predominately produced by the antigen-simulated CD4+ T cells. IL-2 is an important factor for the maintenance of CD4+ regulatory T cells and plays a critical role in the differentiation of CD4+ T cells into a variety of subsets. It has been approved for the treatment of metastatic renal cell carcinoma and metastatic melanoma. While improved IL-2 formulations might be used as monotherapies, their combination with other anticancer immunotherapies, such as adoptive cell transfer regimens, antigen-specific vaccination, and blockade of immune checkpoint inhibitory molecules, for example cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and programmed death 1 (PD-1) mono-antibodies, would held the promise of treating metastatic cancer. Despite the comprehensive studies of IL-2 on boosting anticancer immune system and application of IL-2 for cancer immunotherapy, a number of poignant obstacles remain for future research. Moreover, it is observed in the experimental studies that, mutants of IL-2 has high therapeutic values, termed as "superkine". Thus, the present study focuses on the development of prediction method for IL-2 inducing peptides. Experimentally verified IL-2 inducing & MHC-II binding peptides has been retrieved from the IEDB database for the development of various machine learning models. We hope the developed methods could best serves the need of scientific community for improving the cancer immunotherapy process.