The occurrence of coal-rock dynamic disasters is closely related to the damage state of coal. To achieve real-time monitoring of the crack development trend within coal underload, a localization model for internal fractures based on an equivalent electromagnetic radiation source is constructed. This study explores a monitoring method for fracture development in loaded coal and its corresponding solution algorithm. Additionally, a uniaxial loading experiment is conducted to analyze the effects of coal sample origin and loading rate on the proposed monitoring method, thereby verifying its general applicability. The results indicate that the electromagnetic radiation (EMR) generated by loaded coal originates from both electric-type and magnetic-type sources. The combined effects of these sources contribute to EMR emission during the expansion and closure of internal cracks in the coal. The constructed equivalent EMR source model for charged fractures can be utilized for monitoring coal damage. By employing a triaxial fluxgate sensor, an EMR monitoring array for loaded coal samples can be established. The non-coplanar arrangement of the sensors proves more effective than the coplanar configuration in mitigating error amplification effects. The BFGS-integrated pathfinding strategy (BIPDS) enhances the accuracy of solving typical multimodal functions by more than tenfold compared to traditional algorithms, while reducing the standard deviation by approximately 99%, thereby providing algorithmic support for the proposed radiation source localization model based on vertical distance error. The fracture monitoring model determines a through-crack inclination angle of 78°, with its spatial position closely matching the actual crack distribution. Under different experimental conditions, the variation trends of the synthesized EMR vector intensity remain consistent, indicating that coal sample origin and loading rate do not affect the monitoring performance. The method demonstrates reliable monitoring effectiveness and general applicability. These findings contribute to understanding the damage evolution mechanism of loaded coal and provide technical support for advancing research on the prevention and control of coal-rock dynamic disasters.