Thermal conductivity is an important thermophysical property of nanofluids in many practical heat transfer applications. In this study, a novel approach is proposed to predict the thermal conductivity of nanofluids under multiple operating parameters. The proposed approach may be extended to be used to other thermophysical properties of nanofluids. The Kohonen’s self-organizing maps (SOM), as an unsupervised artificial neural network (ANN), is used to provide an accurate prediction tool for the problem in hand. Furthermore, SOM, similar to any ANN-based approach, can handle nonlinear and complex input–output relationships with high generalization ability. Comparison of the SOM predicted values with corresponding available theoretical results as well as experimental data implies high prediction capability of the developed approach. The proposed approach was utilized to predict thermal conductivity ratio of oxide (Al2O3, CuO and TiO2)/water nanofluids under various operating conditions (nanoparticle size, temperature, and nanoparticle volume fraction).