This project aims to develop a machine-learning model capable of distinguishing between human-generated and AI-generated text. By leveraging various linguistic, syntactic, semantic, and additional text features, the model aims to accurately identify the authorship of given text samples. The implementation involves data preprocessing, feature extraction, model training with hyperparameter tuning, and evaluation using a voting classifier that combines multiple machine learning algorithms.