Environmental ecology stands at the forefront of understanding and addressing the challenges posed by a rapidly changing world. In this context, machine learning, particularly the XGBoost algorithm, has emerged as a pivotal tool, offering unparalleled accuracy and adaptability. This article delves into the origins and workings of XGBoost, highlighting its applications in predicting species distributions, assessing habitat suitability, and modeling climate change impacts. While the benefits of XGBoost, such as high predictive power and robustness to noisy data, are emphasized, the article also sheds light on potential challenges like overfitting and interpretability. The conclusion underscores the importance of a holistic approach, combining domain knowledge with algorithmic prowess, to harness the full potential of XGBoost in environmental ecology.