Feature Selection (FS) has become an essential step in many Machine Learning (ML) platforms. When compared to a single feature selection method, the Ensemble Feature Selection (EFS) method has gained enormous appeal for its ability to select less dimensional, pertinent features. This study presents a comprehensive empirical study examining several papers and showing that the ensemble method outperforms the single Feature Selection methods. The ensemble’s characteristics stand for Effectors of the ensemble technique including single methods that, in general, perform better, thresholds for related subset generation, aggregator methods, and different kinds of datasets. The relationship between classifications and the Ensemble feature selection method stands out in this research. This research’s goal was to show how the best characteristics improve the robustness, stability, and accuracy of classification performance. A crucial data preprocessing method for data science is feature selection. Based on this reasoning and the goal of simplifying feature selection for a classification model and evaluation is styled. To create the best features and demonstrate the classification techniques’ great performance requirements, this study provides the reader with the fundamental concepts regarding attributes that will be used to build an effective ensemble method.