<p>In this study, the influence of Brownian motion and thermophoresis on the flow and heat transfer characteristics of Maxwell nanofluids moving across a stretching plate are examined. Integrating small particles into fluids enhances heat transfer and has substantial applications across energy production and heat transfer devices. The study seeks to examine the effect of Brownian motion, concentration and thermal buoyancy on the heat and mass transfer characteristics of Maxwell nanofluids under particular conditions. The researchers investigate how a fluid which absorbs heat responds to radiative and dissipative effects. The Bayesian Regularization Artificial Neural Network method is employed, a powerful tool capable of accurately addressing challenging fluid dynamics and thermal transport issues in nanofluid-based applications. The transient partial differential equations are converted into nonlinear ordinary differential equations utilizing similarity functions. The improved algorithm is used to solve the set of transformed nonlinear ordinary differential equations after applying the dimensionless variables. The numerical simulations are carried out using MATLAB, yielding quantitative results and accompanying visualizations of the data. We found that Maxwell nanofluids increase the rate at which heat is transferred by about 20–30% over traditional fluids and heat radiation further adds about 10–15% to this improvement under the same working conditions. Fluid velocity decreases by 15–20% as a result of the Lorentz force, however, thermal stability improves with an increase in thermal conductivity by 5–10%.This work highlights the factor that nanoparticle properties play in determining the effectiveness of heat transfer procedures, thereby suggesting possible methods to enhance performance in heat exchange applications. The Bayesian Regularization Artificial Neural Network has been demonstrated to accurately capture fluid motion and heat exchange processes for engineering applications.</p>