This study investigates the thermal behavior and heat transfer characteristics of micropolar nanofluids within inclined containers featuring asymmetric solid boundaries. The system consists of a container with a finite-thickness solid part on the left wall and a solid wall along the right boundary, subjected to an inclined magnetic field and containing a heat source/ sink. Three coupled energy formulations are employed to model the system: the fluid temperature equation, the included medium temperature equation, and the heat conduction equation for solid walls. A comprehensive analysis explores the effects of the length and position of the solid part on thermal performance, using finite difference method simulations. The research introduces a novel approach by developing an artificial neural network to predict heat transfer rates based on numerical data. Key findings demonstrate that relocating the solid part away from the lower edge enhances fluid flow activity while reducing average heat transfer rates. Additionally, increasing the solid part’s length improves convective heat transfer characteristics. The developed ANN model shows excellent predictive capabilities, with target values approaching unity across all studied parameters, validating its effectiveness for thermal performance prediction in such complex systems.