Dengue virus peptides are emerging as potential therapeutics for dengue infection. Due to the important role of dengue peptides in curbing dengue infection, their identification has proven crucial in terms of infection biology. To calculate differences between amino acids and physiochemical attributes, statistical tests and F-scores were used in this work. The random forest algorithm was used to predict dengue peptides using grouped amino acid composition, transition and distribution. Here, we have used three descriptors; Amino acid content, Grouped Amino acid composition and Composition, transition and distribution features (CTDC). We have created models and compared with combined model. Using the grouped amino acid composition as input parameters for the random forest algorithm, Our classifier's overall accuracy increased to 88.80%, which was the greatest overall accuracy found in this investigation. Our classifier produced superior predicting outcomes when compared to previously developed algorithms. In conclusion, we looked at the differences in amino acids and physiochemical properties between dengue viral peptides, using the grouped amino acid composition to build a classifier that predicts these dengue virus inhibitory peptides.