Abstract : Across all industries, digital service platforms have a significant problem from customer attrition. In order to improve churn prediction accuracy and cross-domain generalization, this paper suggests a cross-domain deep learning architecture that makes use of transfer learning. Through knowledge transfer across several industries, including food delivery, streaming, and mobility services, the suggested method enhances predictive performance in areas with less labeled data, allowing for more resilient and flexible churn control solutions. The first dataset used to train a Fully Connected Neural Network (FCNN) had 388 samples and 55 characteristics related to food delivery. The trained model was then applied to two different domains to assess its generalization ability: Uber (50,000 samples, 14 features) and Netflix (1,000 samples, 26 features). Training was conducted using the Synthetic Minority Oversampling Technique (SMOTE) to rectify the imbalance in classes. Significant performance improvements across domains were shown by the transferred model. With a churn recall of 0.79 and an accuracy of 66.73% on the Uber dataset, it outperformed XGBoost and CatBoost by 16.2% and 41.1%, respectively. The recall for Netflix was 1.5% better than CatBoost and 23.2% better than XGBoost. With a churn recall of 0.90 in the source domain (food delivery), the model outperformed XGBoost and CatBoost by 15.4% and 5.9%, respectively. In target domains, the suggested FCNN with transfer learning improved churn recall by up to 10.1%, consistently outperforming both baseline and hybrid models. Because it allows for the early identification of at-risk clients utilizing information transferred from related domains, this method is especially advantageous for sectors with minimal labeled data. In society's expanding digital economy, the concept helps to improve service continuity and minimize corporate losses by increasing proactive client retention.