The purpose of this investigation is to assess the outcome of Oxytactic microorganism in chemical reactive flow of TiO2 + GO/water based hybrid nanofluid flow across a sheet applying artificial intelligence. \({\text{TiO}}_{2}\) and \({\text{GO}}\) nanoparticles are combined with the base fluid, water ( \({\text{H}}_{2} {\text{O}}\) ). There are numerous real-world uses for the concept of artificial intelligence-driven performance improvement of oxytactic microbes in hybrid nanofluid with chemical reaction and thermal radiation in a variety of sectors. By controlling the temperature and chemical conditions for improved performance and focused treatment, it is applicable in biomedical engineering to improve microbial-based drug delivery systems. The model may improve the efficacy of environmental biotechnology’s bioremediation processes, which use microorganisms to degrade pollutants under a range of chemical and temperature conditions. Additionally, the model can improve the efficacy of microbial cultures employed in fermentation or other bio-manufacturing processes in industrial processes like cooling systems by optimizing heat transfer in reactors utilizing nanofluids. The ordinary differential equations can alternatively be resolved utilizing an artificial neural network-based technique with Bayesian regularization. State training, performance, fitting plots, model response, and error histograms plots are utilized to explore the resulting network.
