Customers are the bottom for any commercial enterprise fulfillment and this is why corporations come to be privy to the importance of obtaining pride of clients. Customer churn exercise is important in aggressive and swiftly growing in telecom sector. Due to superior services, competitive pricing, or other benefits offered by competitor companies to clients who sign up, customers move from one telecom service provider to another. The prediction of customer churn has evolved into an essential component of planning processes and strategic decision making in the telecom sector due to the higher costs associated with gaining new consumers. The prime goal line of the introduced study is to scrutinize exactly how a massive bio-inspired data platform might be used to expect customer’s wear and tear in the application of telecom industry. The probabilities of customers leaving a business have been estimated using Mimosa Pudica optimization (MPO) of two upmost classifying approaches. Big data combined with logistic regression and machine learning is also used in this study for comparisons. Feature selection, reduction and clustering are also performed with the help of MPO to forecast customer revenue in the telecom industry, in order to calculate the probability of churn as a function of a collection of factors or client characteristics. In a similar way, MPO is then used to optimize the two customer's churn classifiers on how close their product in every class. This study predicts and analyses churn using data from two different websites. As a result of these judgments, expectations of consumer churn can efficiently provide an accuracy rate of the best classifier as 0.95 percent and an AUC, area under curve of about 0.96 percent.