For telecom companies, customer attrition is a major problem that has a direct impact on retention and revenue. In order to predict churn and take quick retention actions based on customer history, a deep learning model built on Keras and TensorFlow is used for this project. Deep learning enhances the ability to identify intricate data associations when compared to more conventional techniques like logistic regression and decision trees. Data collection, preprocessing, training, and model evaluation are all part of the project. Tenure, charges, and demographics of customers are included in a publicly available data set. Data preprocessing includes feature normalization, categorical variable encoding, and missing value handling. Accuracy and loss criteria are used to train and assess a neural network model with hidden layers. When it comes to identifying risky customers, the deep learning model outperforms the conventional methods due to its high accuracy. The method shows how deep learning can be used in predictive analytics for client retention.