<p>This study presents a comprehensive investigation of nonlinear convection in ferrofluids influenced by couple stresses and spatially variable gravity fields, modeled as linear (<i>z</i>), inverse (−<i>z</i>), and quadratic (<i>z</i>²) profiles in a porous medium. A linear stability analysis using the normal mode technique determines the onset of convection, while a nonlinear energy method is employed to evaluate the stability threshold. The resulting eigenvalue problems are solved using the Galerkin method. The analysis reveals that the combined effects of magnetization, couple stresses, permeability, and gravity variation significantly affect both the linear and nonlinear Rayleigh numbers, thereby altering the extent of subcritical instability. To enable rapid parametric exploration, a multi-output artificial neural network (ANN) model is trained on the analytical solutions. The ANN achieves high predictive accuracy for both stability thresholds, providing a computationally efficient surrogate for real-time assessment. Magnetization and medium permeability enable the onset of convection; couple stresses delay it. Directional gravity variations are found to exhibit distinct stabilizing or destabilizing effects on convective behavior. The results provide new insights into ferroconvection under complex physical conditions and highlight the potential of ANN-based surrogates as scalable tools for real-time stability evaluation in heat and mass transfer systems involving ferrofluids.</p>