## 1 Introduction
These discrepancies make it challenging to establish and gain acceptance for a theoretical model based on physical quantities. Furthermore, there is no unified explanation for the mechanisms underlying the enhancement of thermal conductivity in nanofluids [28]. On the other hand, simulation techniques such as molecular dynamics (MD) and finite element analysis (FEA) provide a more detailed examination of microscale interactions and energy transfer mechanisms. [PERSON] et al. [29] used MD to model copper-argon nanofluid systems and found that the thermal conductivity increases with copper nanoparticle concentration, primarily due to enhanced liquid atom movement rather than slow nanoparticle Brownian motion. FEA studies conducted by [PERSON] et al. [30] showed significant effects of temperature gradients on mass transport and concentration gradients on heat transfer. Although numerical simulations can provide valuable insights, a large number of replicated calculations are inevitable. In addition, the generalization of the numerical models is questioned, i.e., they may not be effectively applied to larger systems or adapt to different fluid components. Consequently, there is a growing interest in applying artificial intelligence methods represented by machine learning (ML) techniques to develop predictive models with obvious advantages in terms of accuracy, reliability, and cost. With its ability to model complex, nonlinear relationships and adapt to diverse datasets, ML models' predictive performance in enhancing the thermal properties of nanofluids is worthy of investigation [31]. For ML approaches, leveraging extensive datasets encompassing various conditions and compositions is an effective way to explore the relationships between features. For instance, [PERSON] et al. [32] combined the least square support vector machine (LSSVM) model and a genetic algorithm (GA) to predict the thermal conductivity ratio of EG-Al2O3 nanofluids, resulting in a high accuracy with a correlation coefficient (R2) value of 0.9902 and a mean squared error (MSE) value of 8.69 \(\times\) 10\({}^{-5}\). Three variables, namely temperature (T), nanoparticle size, and concentration, were used to train the model, which significantly improved the prediction accuracy over existing models. [PERSON] et al. [33] used an artificial neural network (ANN) model to predict the thermal conductivity of various nanofluids developed with water, EG, and transformer oil. A total of 776 experimental data points from 21 sources were used to improve model performance. The results demonstrated that the mean absolute percent error (MAPE) values were 1.26% and 1.44% for training and testing datasets, respectively. [PERSON] et al. [34] used gradient boosting (GB), support vector regression (SVR), decision tree (DT), random forest (RF), and ANN models to predict the thermal conductivity of a TiO2-water nanofluid. The results indicated that the GB model achieved the highest R2 values (0.99) among all models. Besides, the results also highlighted the significance of nanoparticle shape on thermal conductivity. [PERSON] et al. [35] used five ML models to estimate the thermal conductivity and viscosity of water-based nanofluids. The study considered influential nanoparticle properties such as temperature and concentration, and the k-nearest neighbor (KNN) also provided reliable results across both viscosity and thermal conductivity predictions. [PERSON] et al. [36] used a multi-layer perceptron (MLP) model to predict the thermal conductivity of a graphene nanofluid. Note that the influence of temperature (25 \({}^{\circ}\)C to 50 \({}^{\circ}\)C) and weight percentages (0.00025, 0.0005, 0.001, 0.005) on thermal conductivity was explored. The results showed that the MLP obtained higher prediction accuracy and reliability than experimental and theoretical models, resulting in low values of root mean squared error (RMSE) and MAPE. [PERSON] et al.