In thermal and fluid engineering, the behavior of hybrid nanofluids contributes to the development of advanced cooling systems, particularly in electronics and heat exchangers. In thermal engineering, the combined analysis of hybrid nanofluid flow and entropy generation principles play a vital role. For these important applications, this work investigates the production of entropy for hybrid nanofluid stagnant point flow over a Riga plate. Thermal radiation and non-linear convection are both taken into consideration. Hybrid nanofluid is obtained by mixing copper $$( {{\mathrm{Cu}}} )$$ and aluminum oxide $$( {{\mathrm{A}}{{{\mathrm{l}}}_{\mathrm{2}}}{{{\mathrm{O}}}_{\mathrm{3}}}} )$$ nanoparticles into the base fluid glycol $$( {{{{\mathrm{C}}}_{\mathrm{3}}}{{{\mathrm{H}}}_{\mathrm{8}}}{{{\mathrm{O}}}_{\mathrm{2}}}} )$$. The main equations of the problem have been converted to dimension-free form by implementing similarity variables. Artificial neural network (ANN) has been used to solve the resultant equations. It is revealed in this study that velocity has reduced with the upsurge in Grashof number and magnet/electrode factor. Better thermal distribution can be achieved by increasing the volumetric fractions of nanoparticles and the radiation factor, but a rise in the unsteadiness factor can hamper thermal performance for both individual and hybrid nanoparticles. Utilizing nanofluid enhances the delivery and targeted release of therapeutic agents to specific areas in the body, improving control and effectiveness in treating cancer cells.