Liu, ZuozhiAli, Ali B. M.Hussein, Rasha AbedSingh, Narinderjit Singh SawaranAl-Bahrani, MohammedAbdullaeva, BarnoEsmaeili, Sh.2025-03-152025-03-15202500735-19331879-017810.1016/j.icheatmasstransfer.2025.1087492-s2.0-85218419239https://doi.org/10.1016/j.icheatmasstransfer.2025.108749Sawaran Singh, Narinderjit Singh/0000-0001-7067-5239This study looked at ANNs' ability to predict the rheological properties of MWCNT-ZNO / Oil SAE 50 nano lubricant. Five artificial intelligence algorithms-Group Method of Data Handling (GMDH), Extreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Support vector machine (SVM), and Multilayer Perceptron (MLP)-were employed in this work to forecast this nanofluid. The most optimum objective function (mu nf) as an output is the foundation of algorithms used in artificial intelligence. This capacity is developed so that the values predicted by ANN were more consistent with the laboratory numbers by combining GMDH with the metaheuristic approach. This combination enables the metaheuristic algorithm to optimize the evaluation indices and get the predicted data closer to the experimental data by using the GMDH activation parameters as input. For optimization, three metaheuristic algorithms are used, and the combination of GMDH and MOGWO produced the best results. Ultimately, the finest condition that could be achieved is found to have the following input data values: share rate (gamma), temperature (T), and solid volume fraction (phi): 0.0625 %, 50 degrees C, and 5499.6783 s-1 correspondingly.eninfo:eu-repo/semantics/closedAccessNano-LubricantMeta-HeuristicArtificial Intelligence AlgorithmsMetaheuristic AlgorithmArticleQ1Q1163WOS:001435746500001