Feature selection, a method of dimensionality reduction, is nothing but collecting a range of appropriate feature subsets from the total number of features. In this paper, a point by point explanation review about the feature selection in this segment preferred affairs and its appraisal techniques are discussed. I will initiate my conversation with a straightforward approach so that we consider taking care of features and preferred issues depending upon meta-heuristic strategy. These techniques help in obtaining the best highlight subsets. Thereafter, this paper discusses some system models that drive naturally from the environment are discussed and calculations are performed so that we can take care of the preferred feature matters in complex and massive data. Here, furthermore, I discuss algorithms like the genetic algorithm (GA), the Non-Dominated Sorting Genetic Algorithm (NSGA-II), Particle Swarm Optimization (PSO), and some other meta-heuristic strategies for considering the provisional separation of issues. A comparison of these algorithms has been performed; the results show that the feature selection technique benefits machine learning algorithms by improving the performance of the algorithm. This paper also presents various real-world applications of using feature selection.