Mining dust management has traditionally relied on mass-based exposure measurements and comparison with occupational exposure limits. This remains essential for regulatory compliance, but it does not fully describe the physicochemical characteristics that influence lung deposition, toxicity and disease risk. This extended abstract outlines how particle-resolved dust characterisation, combined with automated electron microscopy and artificial intelligence (AI)-assisted analysis, can shift occupational hygiene from reactive compliance monitoring toward risk intelligence. By resolving particle size, morphology, mineralogy and surface-related features, advanced characterisation provides information that is not captured by mass concentration alone. When integrated with conventional sampling and real-time monitoring, this approach can support more targeted control decisions, improve interpretation of exposure data and provide an evidence base for emerging respiratory health risk management across mining environments.