This analysis aimed to classify pitch types (Fastball, Slider, Curveball, Changeup) using various machine learning models, evaluate the performance of those models, and explore the role of different features in making accurate predictions. To achieve this, multiple methodologies and visualization techniques were employed, offering deep insights into the nuances of pitch classification. The results highlight the journey from using a Random Forest model as a baseline to leveraging XGBoost as the optimized model through hyperparameter tuning and feature engineering. This report includes the critical methodologies, challenges encountered, and advanced interpretative techniques used to achieve robust results.