The fundamental objective of any mining operation is to extract the maximum amount of ore in a highly economical way while adhering to safety standards. From an economic and safety perspective, the stability of rock slopes in open pit mine and quarry operations is crucial because unstable slopes have the potential to cause property damage and fatalities. Slope stability in open pit mining operations is crucial for ensuring safety and optimising resource extraction. Traditional monitoring techniques are often labour-intensive and limited in their ability to predict slope failure accurately. Recent advances in Artificial Intelligence (Al) have shown significant potential to enhance slope stability monitoring by providing real-time analysis, predictive capabilities, and optimised decision-making. To ascertain the probability of slope breakdown and how to prevent it, slope stability analysis is a crucial component of mining engineering. There is an immediate need for a method for assessing slope stability that is dependable, affordable, and widely applicable. By examining research work conducted in slope monitoring and testing, there is a need for an alternative approach that makes use of artificial intelligence and machine learning (ML) techniques. The main goal of this scoping review is to determine what has currently been researched and documented about the use of artificial intelligence integrated with machine learning in monitoring slope stability in open pit mines. A scoping review is conducted using electronic bibliographic databases and resources for instance: OneMine, Research Channel Africa, Scopus, ScienceDirect, Web of Science, Taylor and Francis journals, GeoRef, EbscoHost, Proquest, Springer collection, Access World News, World Bank Group and SABINET African Journals, IEEE, were searched to identify peer-reviewed publications, published in English, between January 2011 and February 2025, and related to application of Artificial Intelligence in monitoring of slope stability for open pit mines. The results obtained from the search were treated as follows: The total number of articles obtained and a clear inclusion criterion for the scoping review has been provided. The results show that three main themes have emerged from the previously conducted studies namely; Advanced Machine Learning for Slope Stability Assessment, Optimisation and Hybrid Models for Enhanced Prediction and Real- World Applications and Operational Integration. Future direction of research involves the integration of Artificial Intelligence with real-time monitoring for dynamic slope assessments, to achieve the development of integrated systems, advanced algorithms like ensemble learning and neural networks will be instrumental. There is also a need to develop numerous automated, adaptive machine learning models capable of handling complex data sets from diverse mine environments.