The term Churn describes number of people or valued customers collectively moving out of the subscribed services running under a business organization. The Churn Management mainly aims to diminish the number of potential or valuable customer losses so that the organization highly becomes profitable. In this paper, to propose and design a system which can involve in probable prediction of customer churn using Hybrid Possibilistic and Probabilistic Fuzzy C-Means Clustering Methodology (PPFCM) in combination with enhanced classification. This system effectively improves the prediction accuracy when compared with existing PPFCM-ANN model. This proposed work has two modules: Clustering algorithm are used to reduce the training dataset to any size and the enhanced classification. In the designed clustering module, the input data set is designed into clusters utilizing the benefits of PPFCM clustering algorithm. The obtained clustered information is deployed using enhanced Classifier and the hybrid model is deployed further. During the testing phase, based on the similarity measures or based on the minimum distance the clustered data signifies the most relevant and proficient enhanced Classifier that highlights the nearby cluster of the test data. At last, the output score is used to predict the customer churn.