Accurate prediction of customer churn is critical for optimizing retention strategies and mitigating revenue loss. This study investigates the efficacy of advanced deep learning models—Deep Neural Networks (DNN), Long Short-Term Memory (LSTM) networks, and Autoencoders—in predicting churn from customer data. We utilized a comprehensive preprocessing pipeline to handle missing values and encode categorical variables. DNNs and LSTMs were employed to capture complex patterns in the data, while an Autoencoder was used for feature extraction followed by a classification layer. Model performance was assessed using metrics such as accuracy, F1-score, and confusion matrix analysis. The findings indicate that these deep learning approaches provide significant improvements in churn prediction accuracy, facilitating enhanced decision-making for customer retention strategies.