Efficient thermal management is critical for ensuring the reliability and performance of compact electronic systems, especially those with non-standard and confined geometries such as L-shaped enclosures, where localized hotspots and uneven temperature fields pose serious design challenges. These configurations demand innovative strategies to overcome the limitation of conventional cooling. In order to address this challenge, a hybrid computational framework that integrates high-fidelity computational fluid dynamics (CFD) with an artificial neural network (ANN) has been introduced to analyze magneto-radiative convection and thermal dissipation in a partially porous L-shaped enclosure filled with a ternary hybrid nanofluid (Ag−MoS2−MWCNT−H2O). The proposed framework enables rapid prediction of magneto-radiative convection behavior, reducing computational time by several orders of magnitude. The ANN model, trained with two hidden layers containing 20 and 10 neurons respectively and employing Bayesian regularization, generates predictions in milliseconds, achieving a coefficient of determination of R2 = 0.987 for training and R2 = 0.982 for testing, reducing the computational time compared to the high-fidelity CFD simulations. Our analysis focuses on the numerical investigation of influence of key parameters including Hartmann number (Ha), Darcy number (Da), aspect ratio (Ar), radiation parameter (Rd), magnetic field inclination angle (γ), and nanoparticle shape factor (sf) on flow structure and heat transfer rate, by employing finite difference methodology. Results show that increasing magnetic field strength from 10 to 60 suppresses convective transport by an average of 59.09% due to Lorentz damping. An increase in Da from 10−5 to 10−1 enhances the average Nusselt number by an average of 64.06%. In addition, stronger radiation improves overall thermal transport through radiation-assisted mechanisms. The inclination angle of the magnetic field is also found to be crucial, with vertical orientation exhibiting up to 19.57% weaker suppression of convective heat transfer compared to horizontal orientation. The ANN predictions not only align closely with CFD simulations but also capture intricate insights that traditional models often overlook, demonstrating both efficiency and reliability. The optimal condition for maximum average Nusselt number is identified at Ha=10, Rd=2, γ=64∘, Ar=0.3, Da=10−1.3010, and sf=3. The sensitivity analysis further reveals that the radiation parameter is the most dominant parameter influencing heat transfer performance, followed by the aspect ratio and the Darcy number. The novelty of this work lies in the development of an ANN-integrated CFD approach, which offers a robust and efficient framework capable of accurately capturing magneto-radiative convection in non-standard porous hybrid nanofluid systems. This approach enables real-time prediction and design optimization, surpassing the limitations of traditional numerical methods and provides a powerful and computationally efficient tool for designing next-generation thermal management systems in high-performance miniaturized electronic devices.