In the telecom industry, massive amounts of data are being generated due to the increasing population. Business analysts assert that the expense of acquiring new customers surpasses that of retaining existing ones. The growing population issue has contributed to customer churning. This has raised concern for businesses as they need to compete passionately to retain customers as customers play a vital role in the revenue of a company. If the rate of acquiring new customers fails to match the needs of enterprise development, the collapse of the enterprise is sure, thus early detection of churning aids in taking protective measures for a company to reduce the losses. To determine the optimal choices for anticipating customer attrition before it occurs, this study assesses machine learning methods. The paper discusses capabilities, methodology, results, and ap plications. Principal component analysis is used in conjunction with decision tree and random forest algorithms for classification in the system. This allows the system to study the patterns of customer churn and various factors affecting the trends and patterns. Customer Churn Pattern analysis has the potential to rad- ically change telecommunication industries’ interactions with customers. It has the features to provide better visual patterns on the trends of churning. Feature selection techniques like information gain and correlation analysis among variables are utilized to identify crucial features. These qualities have aided in several domains like businesses, banking, and insurance.