Accurate inversion of the deep initial in situ stress field is a fundamental prerequisite for stability analysis of surrounding rock in underground engineering, roadway support design, and prevention and control of dynamic disasters. To address the problems of scarce in situ stress measurements in deep mining areas, the inability of conventional regression methods to capture the nonlinear characteristics of complex tectonic stress fields, and the tendency of traditional inversion algorithms to fall into local optima and overfitting, this paper proposes an intelligent inversion method based on support vector regression optimized by the artificial bee colony algorithm (ABC-SVR). The artificial bee colony algorithm is employed to adaptively optimize the core parameters of the SVR model, thereby enabling high-precision inversion of complex deep stress fields. Comparing the results with acoustic emission tests demonstrated that the ABC-SVR model significantly outperforms conventional SVR and backpropagation neural networks across various performance metrics. The inversion results show high consistency with the measured data, achieving a root mean square error (RMSE) of 1.25, a mean absolute percentage error (MAPE) of 4.16%, and a coefficient of determination (R2) of 0.908. This method can rapidly reconstruct high-precision initial in situ stress fields in deep unmined regions, providing highly reliable boundary conditions for numerical simulations and demonstrating significant engineering application potential.