The extensive mining area and multitude of working sites in Maping Phosphate Mine result in a complex ventilation system. This complexity manifests as uneven airflow distribution at working faces, posing considerable challenges for efficient ventilation management. An intelligent ventilation management system based on the Python PyQt5 library was developed for Maping Phosphate Mine to improve ventilation efficiency, lower dust concentration at the working face, and enhance safety by addressing uneven air volume distribution. The implementation of an integrated system, comprising a 3D ventilation network model, remote control capabilities, and smart algorithms, has successfully realized zonal planning and on-demand ventilation in the mine’s underground workings. To adapt to the fluctuating air demand at the tunneling face, a remote intelligent control scheme for louvered dampers was implemented. This dynamic demand-based strategy achieves precise distribution of air volume throughout the ventilation network. The research results demonstrate that the system effectively addresses the uneven distribution of air volume, thereby improving the overall ventilation environment and reducing the risk of ventilation-related accidents. The system serves dual purposes: it provides an intelligent ventilation control mechanism and integrates seamlessly with the key subsystems for underground safety production. This synergy is instrumental in advancing the mine’s digitalization and intelligent transformation initiatives. Field test results indicate that the system achieved a 30% reduction in energy consumption and a 70% decrease in dust concentration at the working face, respectively.