Forest cover prediction has applications in environmental monitoring, forest management, and land-use planning. Governments and conservation organizations can use it to assess forest cover types and predict land changes. The research article depicts the use of the XGBoost algorithm applicable in forest cover prediction, focusing on evaluating model performance through key metrics like Mean Squared Error (MSE), logarithmic loss (log loss), and confusion matrices. The XGBoost model, optimized through hyperparameter tuning, demonstrates robust performance with a relatively low MSE, indicating accurate predictions. The log loss value of 0.5786 suggests that while the model’s classifications are reasonably confident, there is room for refinement. The confusion matrix reveals strong performance for certain classes, such as class 1, but highlights significant errors in others, particularly class 5, which shows a high error rate of 60.93%. The proposed model effectively captures underlying data patterns and performs well across most classes. However, further enhancements, such as addressing class imbalances and refining hyperparameters, are needed to improve accuracy in challenging cases. The model’s high hit ratios, where the correct class is often among the top predictions, indicate its reliability in multi-class classification tasks, making it a valuable tool for forest management and environmental monitoring.