Recurrent neural networks (RNN) have attracted attention in the academic community because of their capability to handle intricate, non-linear models. The RNN, with their strong pattern recognition abilities, are therefore well-equipped to be applied in intricate fields such as fluid dynamics, biological computing, and biotechnology. This study investigates the effectiveness of the Levenberg-Marquardt algorithm combined with recurrent neural networks (LMA-RNN) is simulating the heat transfer of a micropolar fluid through a porous medium with radiation (HTMFPMR) model. In this research, data is obtained using the Adams numerical technique and later optimized through the application of LMA-RNN. The LMA-RNN approach divides the data by using 80% for training, 10% for testing, and the remaining 10% for validation purposes. The velocity and temperature distributions are presented, and the effects of the inertia coefficient, micro-rotation, radiation parameter, and Prandtl number on the heat transfer are thoroughly analyzed. An upsurge in the permeability constraint outcomes in a rise in angular velocity and temperature, while causing a reduction in velocity. As the vortex-viscosity constraint increases, both the velocity and angular velocity show an upward trend. The temperature field declines with rise radiation constraint. Mean squared error (MSE), regression plots, and error histograms are used to assess the performance of the LMA-RNN that have been applied. Reduced MSE indicates more accurate model predictions, validating the proposed strategy.