This research presents a robust and comprehensive framework for predicting hybrid nanofluid density using state-of-the-art machine learning and deep learning techniques. Addressing the limitations of conventional empirical approaches, the study utilized a curated dataset of 436 samples from peer-reviewed literature, encompassing nine input parameters such as temperature, volume concentration, and particle densities. Data preprocessing involved outlier removal via the Interquartile Range (IQR) method, followed by augmentation using autoencoder-based reconstruction and Gaussian noise injection, which preserved statistical integrity and enhanced dataset diversity. The research analyzed fourteen predictive models, employing advanced hyperparameter optimization methods facilitated by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). Among machine learning models, Gradient Boosting and CatBoost achieved remarkable accuracy, with R² scores of 0.99999 and minimal MSE values of 0.00091 and 0.00133, respectively. Deep learning models demonstrated exceptional performance, with Recurrent Neural Networks (RNN) achieving R² of 0.9999, MSE of 0.0014, and MAE of 0.012 when augmented with autoencoders. Long Short-Term Memory (LSTM) networks also exhibited superior results (R² and MSE of 0.9998 and 0.1117 respectively). Notably, autoencoder and Gaussian noise augmentation consistently improved predictive accuracy across all models, with autoencoder-augmented XGBoost achieving R² of 0.99997 and MSE of 0.01796. The findings underscore the synergy of advanced data augmentation, meta-heuristic optimization, and modern predictive algorithms in modelling hybrid nanofluid density with unprecedented precision. This framework offers a scalable and reliable tool for advancing nanofluid-based applications in thermal engineering and related domains

Nanofluids are unique thermal fluids with enhanced heat transfer efficiency relative to conventional thermal fluids. The density of nanofluids is an important thermophysical property that determines the heat transfer coefficient of fluids. Hybrid nanofluids are known to show superior thermal properties compared to normal nanofluids, yet investigation on the predictive modeling of the density of hybrid nanofluids is scarce in the literature. In this report, the application of machine learning (ML) for predicting the density of hybrid nanofluids is examined. The considered hybrid nanofluids consist of Al ₂ O ₃ /SiO ₂ , TiO ₂ -SiO ₂ , Fe ₃ O ₄ -MWCNT, Al ₂ O ₃ -CNT, Al ₂ O ₃ -MWCNT, TiO ₂ -MWCNT, CeO ₂ -MWCNT, ZnO-MWCNT, MgO-MWCNT, CuO-MWCNT, Co ₃ O ₄ /rGO, TiO ₂ -MgO, Ag-GNP, and ND-Fe ₃ O ₄ nanoparticles suspended in H ₂ O, GB, DW, and W-EG (60:40%). The ML algorithms examined in this report include: support vector regression optimized with genetic algorithm (SVR-GA), Gradient Boost Regression Algorithm (GBR) with Grid Search Optimization (GBR-GSO), Decision Tree (DT), and Voting Ensemble (VE). These models were developed using the following input parameters: temperature, volume concentration, the density of individual nanoparticles, and density of the base fluid. Excellent correlations of 99.81%, 99.76%, 96.36%, and 95.05% were obtained for the SVR-GA, GBR-GSO, VE, and DT, respectively. This result shows that the SVR-GA had the highest correlations with experimental results. The development of a highly accurate predictive model for the density of hybrid nanofluids is essential because it can facilitate the rapid design of heat transfer devices. Developing a diffusivity model for hybrid nanofluids using a machine learning approach is a possible extension of the present study.