The telecommunication industry, with its rapid technological advancements and increasingly competitive market, faces the critical challenge of customer churn. This study aims to explore the potential of big data analytics in predicting customer churn within the telecommunication sector. By employing advanced data mining techniques and machine learning algorithms, this research endeavors to identify patterns and correlations in customer behavior that can be indicative of churn risk. The analysis involves the collection and integration of large-scale customer data, encompassing various dimensions such as service usage, billing history, and customer feedback. The findings suggest that a combination of predictive analytics and customer segmentation can significantly enhance the accuracy of churn predictions. This paper contributes to the field by proposing a comprehensive framework for churn prediction, which not only helps telecommunication companies in retaining customers but also aids in optimizing their service offerings and marketing strategies. Ultimately, the adoption of such predictive models can lead to substantial cost savings and improved customer satisfaction.