The dispersion of nanomaterials in oils can improve the thermal storage and energy conversion capabilities of nano-synthetic oils. The zinc oxide (ZnO)-oil nanofluids can explore extensive industrial applications, especially in heat transfer processes, the food industry, and medicine. The zinc oxide nanoparticles are functionalized with oleic acid using surface-modification non-polarization approach to improve the stability of nanofluids. Different surface characterizations of the functionalized zinc oxide (f-ZnO) nanoparticles are presented. The presence of the carboxylic group on the surface of f-ZnO nanoparticles is analyzed by Energy Dispersive X-ray (EDX) and Fourier Transform Infrared (FTIR) spectroscopy. The f-ZnO nanoparticles are dispersed in highly refined paraffinic mineral oil at different concentrations (0�1 wt) using the two-step method and ultrasonication. The dispersion stability is examined by sedimentation and Dynamic Light Scattering (DLS) methods for pure ZnO-oil- and f-ZnO-oil nanofluids and improved dispersion stability of the f-ZnO-oil nanofluids is observed. The thermal properties of the stabilized f-ZnO-oil nanofluids at different temperatures and concentrations are experimentally measured. These thermophysical properties include effective density, rheological behavior, and effective thermal conductivity. The effective coefficient of thermal expansion for different concentrations of nanofluids is obtained from density measurements using the conventional model. The rheological studies suggest that the nanofluids show higher viscosity than pure oil and exhibit shear-thickening behavior. Improvement in thermal conductivity of up to 18 is observed for 0.5 wt of f-ZnO-oil nanofluid. The experimental data for thermophysical properties are compared with the existing conventional models of nanofluids to check the effectivity of theoretical models in the case of f-ZnO-oil nanofluids. The multi-variable new generalized correlations for effective density and effective viscosity of f-ZnO-oil nanofluids are proposed. © 2019 Elsevier B.V.