This chapter provides a comprehensive review of the applications of Artificial Intelligence (AI) in Natural Resource Management (NRM). It explores how AI, combined with technologies like remote sensing, the Internet of Things (IoT), and big data analytics, is transforming traditional approaches to NRM by enhancing data collection, processing, and interpretation capabilities. AI algorithms, particularly machine learning and deep learning models, demonstrate remarkable accuracy in analyzing complex datasets, extracting valuable insights, and making precise predictions, thereby facilitating informed and timely decision-making. The chapter examines various applications of AI in NRM, including water resource management, forest management, mineral and mining management, and biodiversity conservation. Specific examples include the use of Convolutional Neural Networks (CNNs) for land cover classification and deforestation monitoring, Long Short-Term Memory (LSTM) networks for streamflow prediction and flood forecasting, and AI-powered object detection algorithms for monitoring wildlife populations and detecting illegal activities. The chapter also discusses the benefits of AI in NRM, such as improved efficiency and accuracy through automation, enhanced data utilization through the integration of diverse data sources, cost savings through optimized resource allocation, and increased accessibility to advanced analytical tools for a wider range of stakeholders. However, the chapter acknowledges the challenges and considerations associated with AI implementation, including data quality and availability, ethical and privacy concerns, integration with existing systems, and skill requirements. Addressing these challenges requires collaborative efforts among scientists, AI researchers, policymakers, and stakeholders to develop robust, interpretable, and ethically aligned AI systems for environmental applications. The chapter concludes by emphasizing the transformative potential of AI in revolutionizing NRM and ensuring the responsible and sustainable use of Earth's resources.