<p>The formation of nanofluids significantly improves the thermophysical characteristics of base fluids, leading to several technical benefits. The conventional method of analyzing thermal fluid characteristics often involves cost, a variety of operating conditions, and time-consuming experiments. To address these limitations, this study proposes a specialized machine learning algorithm, gradient boosting regression (GBR), utilizing grid search optimization (GSO), to precisely estimate the density of nanofluids. This property has received relatively less attention of researchers in comparison with other thermophysical properties. The model examines Al₂O₃, CeO₂, and CuO nanoparticles, dispersed in the ethylene glycol base fluid. The proposed model accurately predicts nanofluid densities, achieving a correlation coefficient of 99.99% for training and 99.95% for testing, indicating a high degree of accuracy. It has a mean absolute deviation (MAD) of only 0.0480, in contrast to the MAD value of 4.909 of Pak and Cho model, and the low value of root-mean-square error of 0.00157 exhibits significant improvement and validates the efficiency of proposed model. To assess the effectiveness of the proposed model, nanoparticle size, volume concentration, and temperature are used as key factors from 260 experimental data counts. The outcomes clearly show that the GBR–GSO model’s predictions are highly accurate and superior to conventional theoretical models, demonstrating ability of the model, to effectively capture the complex relationships between these factors and nanofluid densities and can address numerous challenges that exist in industries at a relatively low cost.</p>