<p>The transition toward autonomous mining operations places path planning at the centre of fleet automation, as it governs the safe, efficient, and sustainable movement of ground vehicles across complex mine environments. This study presents the first systematic review focused exclusively on path planning for mining vehicles, synthesising 23 high-quality studies published between 2015 and 2024. The review evaluates a broad spectrum of algorithms—including deterministic approaches, sampling-based methods, optimisation techniques, and bio-inspired or learning-based strategies—while comparing their strengths, limitations, and readiness levels for mining deployment. Significant progress has been made in path planning for Load-Haul-Dump (LHD) machines and haul trucks; however, critical research gaps remain for underexplored equipment such as open-pit drill rigs, bulldozers, and draglines. The review highlights the pressing need for further research into integrating advanced path-planning algorithms with real-time control systems, while also emphasising the importance of supporting technologies and practical validation to bridge the gap between research prototypes and reliable mining operations. In particular, this study provides a detailed examination of path planning for open-pit drill rigs, underscoring the gaps that must be addressed to achieve full autonomy. Finally, the paper stresses the strategic importance of path planning for advancing environmental, social, governance (ESG) objectives, enabling fleet management integration, and supporting the transition toward zero-entry mining systems, while outlining a research agenda that positions fully autonomous drill rigs as the next frontier of mining automation.</p>