To address the challenges of insufficient nonlinear inversion accuracy and low convergence efficiency in grounding grid defect detection using the transient electromagnetic apparent resistivity method, this study proposes an intelligent hybrid inversion approach that integrates the global search capability of genetic algorithms (GA) with the local optimization of Newton’s method. To overcome the limitations of traditional GA, such as slow convergence and low sensitivity to small-scale defects, a “data-driven and model-constrained” inversion framework is established. This framework employs reactive mechanisms in GA, including tournament selection and dynamic crossover and mutation, to mitigate the “black-box mapping” limitations of purely data-driven models and achieve interpretable searches in the initial solution space. High-quality initial values obtained from the global search are then used as inputs for Newton’s method, fundamentally resolving the “initial value sensitivity” issue of traditional iterative approaches and establishing a collaborative inversion strategy of “global pre-search and local refinement.”Experimental results demonstrate that, under scenarios involving 2 000 to 10 000 data groups, the hybrid algorithm achieves an average total computation time of 7.2~35.8 seconds, representing a reduction of 4.8~12.6 seconds compared to the combined time of traditional GA and Newton’s method (12.0~48.4 seconds). Iterative efficiency is significantly improved, with cases terminating at the preset maximum iteration limit (100 cycles) reduced by 33.6% compared to Newton’s method, and the proportion of valid solutions obtained within fewer than 20 iterations increased by 45-4%. The inversion accuracy is notably superior, with an average error of 6.020 7×10-8, reflecting reductions of 83.65% and 98.95% compared to traditional iterative methods and GA, respectively. Finally, field scaled-model experiments confirm that the proposed method can effectively identify grounding grid topologies and detect hidden defects such as fractures and gaps, significantly enhancing detection accuracy and efficiency under complex operational conditions compared to single-method approaches.