Signals and Telecommunication Journal Dit Suthiwong 15 1 2026 https://doi.org/10.6025/stj/2026/15/1/29-39 https://www.dline.info/stj/fulltext/v15n1/stjv15n1_3.pdf This study investigates machine learning approaches for customer churn prediction and segmentation in the telecommunications sector, addressing the critical business challenge of subscriber retention amid rising acquisition costs. Leveraging the Telco Customer Churn dataset, comprising 7,043 customer records with 21 demographic, service usage, and billing attributes, we implement a dual method framework that combines supervised classification and unsupervised clustering. A Support Vector Machine (SVM) with a radial basis function kernel models nonlinear relationships between customer attributes and churn behavior, achieving 79.18% accuracy and a ROC-AUC of 0.797. As an interpretable benchmark, Logistic Regression delivers superior ranking performance (ROC-AUC: 0.84) with 80.77% accuracy and a churn recall of 0.578, enabling actionable insights through coefficient analysis. Complementing prediction, K-means clustering identifies three distinct customer segments High Value Loyalists, Budget Newcomers, and Mid Tier Growth based on tenure, charges, and service adoption patterns. Cluster validity is confirmed through silhouette analysis (optimal K=3, score 0.52) and PCA visualization. The segmentation reveals strategic opportunities: loyalty programs for high value customers, onboarding support for at-risk newcomers, and cross selling for growthoriented mid tier users. Results demonstrate that effective churn management requires integrating predictive accuracy with nuanced customer understanding. While nonlinear models capture complex behavioral patterns, interpretable linear models offer practical advantages for operational deployment. This research provides a robust analytical foundation for data driven retention strategies, enabling telecom providers to transition from reactive interventions to proactive, personalized customer relationship management that enhances long term profitability in competitive markets.