Customer churn is customer loss to competing services and remains a critical concern for telecommunications firms and subscription based providers alike. As markets liberalize and digital transformation accelerates, all these emerging technologies and increasing competition have made retaining customers more complex than ever. Even a slight change in churn rates can translate into serious decline in revenue and operational challenges. This paper investigates churn prediction through analysis of two distinct datasets: a telecom dataset and a subscription platform dataset. We performed an array of machine learning methods including ensemble learners, linear models, tree based classifiers, probabilistic frameworks and hybrid approaches. By evaluating each model's performance before and after hyperparameter tuning, we demonstrate that feature engineering and optimization resulted in improved accuracy. Our findings highlight the value of combining advanced algorithms with processed data sets and parameter adjustments. Ultimately these insights lay the groundwork for more effective customer retention strategies in highly competitive service industries.