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World widely, cancer is considered a fatal disease and remains the major cause of death. Conventional medication approaches using therapies and anticancer drugs are deemed ineffective due to its high cost and harmful impacts on the normal cells. However, the innovation of anticancer peptides (ACPs) provides an effective way how to deals with cancer affected cells. Due to the rapid increases in peptide sequences, truly characterization of ACPs has become a challenging task for investigators. In this paper, an effort has been carried out to develop a reliable and intelligent computational method for the accurate discrimination of anticancer peptides. Three statistical feature representation schemes namely: Quasi-sequence order (QSO), conjoint triad feature, and Geary autocorrelation descriptor are applied to express motif of the target class. In order to eradicate irrelevant and noisy features, while select salient, profound and high variated features, principal component analysis is employed. Furthermore, the diverse nature of learning algorithms is utilized in order to select the best operational engine for the proposed model. After examining the empirical outcomes, support vector machine obtained quite encouraging results in combination with QSO feature space. It has achieved an accuracy of 96.91% and 89.54% using the main dataset and alternative dataset, respectively. It is observed that our proposed model shows an outstanding improvement compared to literature methods. It is expected that the developed model may be played a useful role in research academia as well as proteomics and drug development. • Computational model is developed for prediction of anticancer peptides. • Quasi-sequence order, conjoint triad feature, and Geary autocorrelation descriptor are used as feature extraction scheme. • PCA is applied as feature selection scheme. • Various classification algorithms are utilized. • Proposed model achieved quite promising results. [ABSTRACT FROM AUTHOR]