This chapter explores the integration of Machine Learning (ML) with Extended Reality (XR) technologies to develop advanced environmental prediction models. As environmental challenges grow increasingly complex, combining ML and XR offers a robust approach to forecasting and mitigating ecological impacts. The chapter begins with an overview of XR technologies—Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—and their applications in environmental science. It examines how ML algorithms enhance prediction capabilities through supervised and unsupervised learning techniques, enabling the analysis of large datasets and the simulation of environmental scenarios. Case studies illustrate successful implementations, such as air quality forecasting and natural disaster impact predictions. Challenges related to data quality and model accuracy are discussed, along with strategies for overcoming them. The chapter concludes by highlighting the need for ongoing research and innovation in ML and XR to produce more accurate, interactive, and actionable insights for addressing critical environmental issues.