This study investigates the prediction of viscosity and thermal conductivity (TC) in silica-alumina-MWCNT/water nanofluids (NFs) using artificial neural networks (ANNs) and various evolutionary algorithms. Six optimization algorithms—MOGOA, MALO, MOMFO, MOWOA, MOPSO, and NSGA II—are employed to predict and model the μNF and TC. The correlation between design variables (φ and Temp) and objective functions is analyzed, with φ showing a higher influence on μNF and TC (0.83 and 0.92, respectively) compared to Temp (−0.5 and 0.38). The NSGA II algorithm, when coupled with ANN and GMDH, performs best in predicting μNF and TC, achieving a maximum deviation of −0.108 and R² values of 0.99996 and 1 for μNF and TC, respectively. A meta-heuristic Genetic Algorithm (GA) is then used to minimize μNF and TC, identifying optimal conditions at points A and D on the Pareto front. Point A, characterized by low φ and Temp (0.0002 and 50.8772, respectively), yields μNF and TC values of 0.9988 and 0.6344, while point D, with high φ and Temp (0.49986 and 59.9775, respectively), results in μNF and TC values of 2.382 and 0.8517. This study provides insights into the optimal operating conditions for maximizing NF performance.This study investigates the prediction of viscosity and thermal conductivity (TC) in silica-alumina-MWCNT/water nanofluids (NFs) using artificial neural networks (ANNs) and various evolutionary algorithms. Six optimization algorithms—MOGOA, MALO, MOMFO, MOWOA, MOPSO, and NSGA II—are employed to predict and model the μNF and TC. The correlation between design variables (φ and Temp) and objective functions is analyzed, with φ showing a higher influence on μNF and TC (0.83 and 0.92, respectively) compared to Temp (−0.5 and 0.38). The NSGA II algorithm, when coupled with ANN and GMDH, performs best in predicting μNF and TC, achieving a maximum deviation of −0.108 and R² values of 0.99996 and 1 for μNF and TC, respectively. A meta-heuristic Genetic Algorithm (GA) is then used to minimize μNF and TC, identifying optimal conditions at points A and D on the Pareto front. Point A, characterized by low φ and Temp (0.0002 and 50.8772, respectively), yields μNF and TC values of 0.9988 and 0.6344, while point D, with high φ and Temp (0.49986 and 59.9775, respectively), results in μNF and TC values of 2.382 and 0.8517. This study provides insights into the optimal operating conditions for maximizing NF performance.