Customer churn poses a significant challenge for companies, impacting both revenue and sustained growth. This project aims to create a machine learning model that predicts customer churn, enabling businesses to pinpoint individuals who are likely to leave and implement effective retention strategies. We utilize the Telco Customer Churn dataset, which contains valuable information such as customer demographics, contract specifics, service usage patterns, and billing data. The dataset undergoes preprocessing, including the handling of missing values, encoding of categorical variables, and standardization of numerical features. We train and evaluate various machine learning algorithms, such as Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting (XGBoost). Models are measured using metrics like accuracy, precision, recall, and F1-score to identify the best performer. Our findings reveal that factors such as contract type, tenure, monthly charges, and associated services have a strong impact on churn behavior. The refined model allows businesses to identify customers at high risk of leaving and take proactive measures, including personalized promotions, enhanced customer service, and loyalty incentives to mitigate churn. This research underscores the value of machine learning in generating precise churn predictions, empowering companies to make informed, data-driven decisions. Future developments may involve hyperparameter tuning, feature engineering, and real-time model deployment to further enhance predictive accuracy and business efficacy.