The telecom sector faces a serious problem with customer attrition, which has an impact on retention tactics and profitability. To more accurately forecast churn, this paper research examines artificial intelligence (AI) and big data analytics methodology combination. By leveraging machine learning algorithms and vast datasets, the research focuses on identifying patterns in customer behaviour, network usage, billing trends, and service interactions. The methodology involves preprocessing data, feature selection, and model training using tools such as Apache Mahout, Hadoop, and the Random Forest algorithm. The results demonstrate that AI-driven analytics improves churn prediction accuracy by uncovering nuanced insights that traditional methods often miss. The study also highlights how telecom providers can implement proactive retention strategies by identifying at-risk customers in real time. This approach not only enhances customer satisfaction but also reduces churn-related revenue losses. The findings underscore the transformative potential of AI and Big Data in optimizing telecom operations and fostering a competitive advantage in a dynamic market. This paper serves as a guide for telecom providers seeking to adopt advanced analytics to address churn and improve customer loyalty.