A technique called feature selection, often referred as attribute subset selection, selects the optimal subset of features for a given set of data by reducing the dimensionality and eliminating unnecessary characteristics. There can be 2n feasible solutions for a dataset with “n” features that is challenging to address using the conventional attribute selection method. Meta heuristic-based approaches perform better than traditional procedures in such situations. Numerous evolutionary computing techniques have been effectively used in Feature Selection challenges. On the distribution of options in the choice space, some research has been done. Achieving one, however, necessitates advancing the other since many optimisation problems contain two or more competing goals. The multi-objective optimisation technique discussed in this research finds the best effective trade-off between numerous objectives. Multi-objective Programs require multiple non-dominated solutions that could be found as opposite to just one. In the initial stage, we applied the Grey Wolf Optimisation (GWO) to acquire the optimised features. On the basis of the features selected, we trained the classifiers—Support Vector Machine (SVM) and Random Forest (RF) in the second phase. Experiment has been carried out on three benchmark datasets namely Glass, Wine and Breast Cancer Datasets retrieved from the UCI repository to show the supremacy of the proposed technique, the effectiveness of the recommended feature selection approach has been evaluated. The testing results show that the suggested GWO with Random Forest performs better than GWO with SVM.