The clearance of explosive remnants of war (ERW) continues to be a predominantly manual and high-risk process that can benefit from advances in technology to improve its efficiency and effectiveness. In particular, research on artificial intelligence for ERW clearance has grown significantly in recent years. However, this research spans a wide range of fields, making it difficult to gain a comprehensive understanding of current trends and developments. Therefore, this article provides a literature review of academic research on AI for ERW detection for clearance operations. It finds that research can be grouped into two main streams, AI for ERW object detection and AI for ERW risk prediction, with the latter being much less studied than the former. From the analysis of the eligible literature, we develop three opportunities for future research, including a call for renewed efforts in the use of AI for ERW risk prediction, the combination of different AI systems and data sources, and novel approaches to improve ERW risk prediction performance, such as pattern-based prediction. Finally, we provide a perspective on the future of AI for ERW clearance. We emphasize the role of traditional machine learning for this task, the need to dynamically incorporate expert knowledge into the models, and the importance of effectively integrating AI systems with real-world operations.