In this study, a new hybrid method is proposed by combining an unsupervised learning method, Fuzzy C-Means clustering (FCM) that can be customized for operating on different types of data with the Generalized Normal Distribution Optimization Algorithm (GNDOA). In this study, we address problems that have been need to be solved in traditional feature selection and clustering practices. In the proposed GNDOA-FCM, normalized distribution is being used to detect survey area effectively followed by the most efficient clustering results generated through FCM. This hybrid GNDOA-FCM approach is evaluated using benchmark datasets via extensive analysis. By metrics feature selection accuracy and silhouette degree, the results are compared with those of current clustering methods. The results not only demonstrate that we can attain higher clustering quality, but it also shows that the algorithm is very flexible and has significant benefits over the classical algorithms. This research shows that hybrid GNDOA-FCM algorithm provides ample results of data analysis and practical solutions to optimizing applications; thus, it is a promising answer to unsupervised learning issues.