Customer churn is a major issue for SaaS companies because it comes on top of their revenue, and that affects their growth. To tackle this issue, our study examines how integrating data processing pipelines into machine learning models can improve customer churn prediction. Using Telco customer churn dataset as a case study, we demonstrate how pipelines enhance scalability for SaaS and similar subscription-based sectors. In this study, we compared four popular machine learning models: XGBoost, LightGBM, Random Forest, and Decision Tree, using both traditional non-pipeline and pipeline methods. Quantitative results showed comparable accuracy between approaches and around 84.6% for the best performing Random Forest model. But the real advantage of using pipelines is that they make the process more reliable and easier to manage. Additionally, they can also lead to a slight reduction in training time. Moreover, the automated pipeline ensures that preprocessing steps (such as scaling and balancing) are consistently applied during training and testing, which reduces human error and makes it easier to deploy it in the real-world settings. According to these results, pipeline integration offers a more reliable, scalable, and ready-to-use method for predicting customer churn in the SaaS industry.