In this study, we conduct a detailed examination of customer churn prediction for a telecom business, Telco Communication, using a dataset of 7043 customer records. The study analyzes various demographic and service-related factors to identify the primary drivers of customer attrition. Leveraging advanced machine learning techniques, we aim to develop a prediction model that accurately identifies clients most likely to leave, enabling the company to create targeted retention strategies. Our findings indicate that attributes such as contract type, tenure, and monthly charges significantly influence churn behavior. To forecast customer attrition, we employed multiple machine learning models, including Random Forest, SVM, KNN, Naive Bayes, Decision Tree, and Logistic Regression. Among these, Random Forest achieved the highest accuracy at 81%, followed by SVM at 79.7%, and Logistic Regression at 79%. Although Random Forest outperformed the other models in accuracy, Logistic Regression was particularly valuable for its interpretability, offering insights into how individual factors affect churn likelihood. These findings provide actionable insights for the telecom industry, laying a strong foundation for developing effective customer retention strategies.