The Review Article on the Exploration of Artificial Intelligence and Machine Learning in Mineral Exploration

Abstract

Analyzing massive volumes of data is essential in mineral prospecting, which is a difficult and intricate process. Conventional
approaches to mineral discovery are labor-intensive, costly, and often unsuccessful. The advent of AI and ML, however, opens
the door to a potential paradigm shift in the mining sector. This review article delves into the most recent cutting-edge uses of
artificial intelligence and machine learning in mineral exploration, assesses their strengths and weaknesses, and highlights the
advantages and disadvantages of using these technologies. The research shows that mining initiatives may be far more efficient
and successful when using AI and ML approaches. In the field of mineral exploration, a number of AI and ML techniques are
now in use, including Neural Networks, Decision Trees, and Random Forests. To cut down on the time and money spent on
mineral exploration, these methods aid in finding connections and patterns in the massive amounts of data. The report also
points out some possible drawbacks, such how important it is to have high-quality data, how difficult it is to understand the
findings, and how important it is to address ethical concerns when utilizing ML and AI in mining. There are major ramifications
for the mining sector in this study’s conclusions. Mineral exploration that makes use of AI and ML may have positive effects
on society and the environment while also increasing profits and decreasing expenses. Research and development of artificial
intelligence and machine learning methods for mineral exploration may be advanced according to the study’s suggestions. Ultimately,
the mining sector stands to undergo a sea change if artificial intelligence and machine learning are not included into
mineral exploration. Their potential is vast.