Abandoned mining sites in Africa have caused severe environmental, social and economic problems, including soil degradation, water contamination and safety hazards. This study aimed to investigate the adoption, impacts, and implementation strategies of Artificial intelligence (AI). for managing abandoned mining sites. The objectives were to identify factors influencing Artificial Intelligence adoption, analyse its impacts, examine adoption challenges and establish strategies for effective implementation of environments. The study is novel as it combined bibliometric analysis and qualitative insights from secondary data to provide a multi-dimensional understanding of AI in abandoned mining sites management. The study was guided by the Technology Acceptance Model and Innovation Diffusion Theory. Data was collected from Scopus indexed Journals and Web of Science, with bibliometric analysis revealing publication trends-authorship networks, and thematic clusters, while qualitative interpretation assessed impacts, challenges and strategies. Findings showed that technological readiness, environmental monitoring, socio-economic pressures, regulatory frameworks and industry 4.0 integration drove AI adoption .AI enhanced environmental restoration, risk prediction, operational efficiency and decision -making. Adoption was constrained by data and technology gaps, financial barriers, skills shortages, weak policies and community resistance. Effective strategies included stakeholder engagement, infrastructure investment, capacity building, industry 4.0 integration and regulatory support. The study concluded that AI provides multi-dimensional benefits but requires coordinated technological, institutional and community efforts. Recommendations include strengthening technology, training, policy and stakeholder participation. Future research should focus on country specific case studies, long term impact evaluation, policy effectiveness and interdisciplinary approaches to improve sustainable mining management.