Roadways serving island longwall panels are highly susceptible to severe asymmetric deformation under extreme eccentric loading from multiple adjacent goafs. To address the difficulties in characterizing the surrounding rock load imbalance and the high false-negative rates of conventional algorithms under extremely imbalanced monitoring data, an intelligent assessment method integrating spatially asymmetric physical features with cost-sensitive learning was developed. Implicit equation analysis and numerical simulation clarified the mechanical mechanism by which principal stress axis deflection induces butterfly-shaped asymmetric rotational failure, enabling the construction of dimensionless integrated asymmetry and structural transfer asymmetry coefficients. Reconstruction of the cost-sensitive objective function increased the recall rate for hazardous samples from 37.5% (baseline model) to 92.2%, while maintaining a precision of 96.7%. Following the field implementation of a three-tier differentiated roadway control scheme, the integrated asymmetry coefficients at critically eccentrically loaded stations (i.e., Station 12 and Station 07) remained below 0.2 during the monitoring period, enabling real-time intelligent perception and proactive stability control of roadways subjected to complex eccentric loading. Ultimately, this study confirms the viability of integrating physics-informed features with machine learning, demonstrating significant potential for advancing the transition toward intelligent and proactive safety management in complex underground construction.