Fully Homomorphic Encryption (FHE) is a ground breaking cryptographic technique that allows computations to be performed directly on encrypted data, preserving privacy and security. This paper explores the application of Fully Homomorphic Encryption on Extreme Gradient Boosting (XGBoost) multiclass classification, demonstrating its potential to enable secure and privacy-preserving machine learning. The paper presents a framework for training and evaluating XGBoost models using encrypted data, leveraging FHE operations for encrypted feature engineering, model training, and inference. The experimental results showcase the feasibility of applying Fully Homomorphic Encryption to XGBoost-based multiclass classification tasks while maintaining data confidentiality. The findings highlight the trade-off between computation complexity and model accuracy in FHE-based approaches and provide insights into the challenges and future directions of utilizing Fully Homomorphic Encryption in practical machine learning scenarios. The study underscores the significance of privacypreserving machine learning techniques and paves the way for secure data analysis in sensitive domains where data privacy is of utmost importance.