Background With the rapid advancement of nanotechnology and the expansion of its applications in various fields, nanofluids have recently been proposed as a novel strategy for heat transfer operations, and extensive research has been conducted in this regard. Ternary hybrid nanofluids (HNFs) can be used in cooling electronic chips, automotive radiator, solar collectors, heat exchangers, heat pipes, and refrigeration systems. Methods The current study investigates the efficacy of sonication time (ST) on the viscosity and thermal conductivity of a ternary HNF consisting of GO-Fe3O4-TiO2 nanoparticles (NPs) dispersed in the hydraulic oil HLP 68. Nanoparticle volume fractions (NPVFs) of 0.05–1%, STs of 30–120 min, and mixing ratios (MRs) of 1:1:1, 1:1:2, 2:1:1, and 1:2:1 are considered. Moreover, the Support Vector Regression (SVR) and Group Method of Data Handling (GMDH) machine learning algorithms are used to accurately estimate the thermal conductivity and viscosity based on the available research data. Significant findings The outcomes indicated that the viscosity and thermal conductivity of the HNF increase with an intensification in the NPVF. The highest value of viscosity and thermal conductivity were detected for the MR of 2:1:1 and 1:1:1, respectively. The ST was found to have a significant impact on the viscosity and thermal conductivity, which is dependent on the MR and NPVF. The minimum viscosity and maximum thermal conductivity were observed at an ST of 90 min. The highest impact of ST on the viscosity and thermal conductivity was observed for an NPVF of 0.05%. By increasing the ST from 30 to 90 min, thermal conductivity increased between 10.07% (at MR = 1:2:1 and φ = 1%) to 14.82% (at MR = 1:1:1 and NPVF = 0.05%) and viscosity decreased between 1.94% (at MR = 2:1:1 and NPVF = 0.5%) to 24.27% (at MR = 1:1:1 and NPVF = 0.25%). The modeling results showed that the R² value for thermal conductivity and viscosity is 0.9582 and 0.9832 using SVR, and 0.9531 and 0
