Mining activities are associated with negative environmental, social, and economic impacts and require enhanced monitoring and detection systems. This chapter systematically reviews the Deep Learning (DL) approach in detecting and monitoring mining activities. It explores the development of DL techniques for analyzing remotely sensed data and enabling real-time observation of mining activities, incorporating Internet of Things (IoT) devices. The review also highlights key DL models like Convolutional neural networks (CNNs) and Recurrent neural networks (RNNs), which have been used in satellite imagery, UAV, and sensor networks. Additionally, the chapter examines case studies on illegal mining, focusing on their socio-environmental impacts and the effectiveness of DL in addressing these issues. Challenges related to data availability, computational requirements, and model size are explained, and potential future developments aimed at developing synergies with the help of other sophisticated technologies, including AI, IoT, and blockchain for improving mining supervision and resource utilization are explored. The current review focuses on offering guidance to researchers and policymakers on the opportunities and challenges in improving sustainable mining using DL and IoT.