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Applications of AI and machine learning in mining: digitization and future directions

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Abstract

The mining industry is an important contributor to the country’s overall economic health and prosperity. However, there are a number of problems including low productivity, excessive costs, environmental degradation, social conflicts, and regulatory uncertainties that the industry must overcome. To overcome these challenges, the industry must adopt new technologies that can enhance performance, efficiency and sustainability. These technologies include the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), robotics, augmented reality (AR), and virtual reality (VR). Technological developments are driving a period of digital transformation in the mining industry. Improving efficiency, safety, and sustainability are greatly aided by digitization in conjunction with Artificial intelligence (AI) and machine learning (ML) approaches. This paper is an attempt to highlight the mining industry problems which were being solved by adopting the technological innovations through digitization, and the benefits of use of IoT, autonomous system and AI-ML models and presents few case examples with successful application of IoT, autonomous system and AI-ML methods. This paper also describes about the challenges faced for adopting the technologies and the comparison between existing practices and innovative practices.

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Authors and Affiliations

  1. Department of Mining Engineering, National Institute of Technology, Rourkela, India

    Debi Prasad Tripathy

  2. Department of Mining Engineering, Centurion University of Technology and Management, Odisha, India

    Arun Kumar Sahoo

Authors
  1. Arun Kumar Sahoo
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  2. Debi Prasad Tripathy
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Contributions

AK Sahoo wrote the main manuscript text and DP Tripathy reviewed the manuscript.

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Correspondence to Arun Kumar Sahoo.

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Sahoo, A.K., Tripathy, D.P. Applications of AI and machine learning in mining: digitization and future directions. Saf. Extreme Environ. 7, 4 (2025). https://doi.org/10.1007/s42797-025-00118-1

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  • DOI: https://doi.org/10.1007/s42797-025-00118-1

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