With the exponential rise of mobile applications in recent years, predicting the success rate of an app before or shortly after launch has become a crucial challenge for developers, investors, and marketing strategists. This study explores machine learning techniques to build predictive models capable of estimating a mobile app's success based on features such as app category, user ratings, number of installs, review sentiment, update frequency, and monetization strategy. We analyze a publicly available dataset and apply supervised learning methods including Random Forest, Logistic Regression, and Gradient Boosting. The model is evaluated based on precision, recall, and F1 score, with SHAP values used for interpretability. Our results demonstrate that user reviews and app ratings have the strongest predictive power. By providing a predictive and explainable model, our research offers valuable insights for stakeholders in the mobile app ecosystem aiming to optimize release strategies and investment decisions.