Artificial Intelligence (AI) algorithms are becoming beneficial as substitute methods to conventional approaches or as components of incorporated systems. They have been utilized for solving complex applied problems in different fields and are becoming more and more popular at present. AI approaches can learn from patterns; are fault tolerant in the sense that they are capable to handle noisy data; are capable to manage non-linear problems; and once learned can carry out generalization and estimation at great speed. In this survey, for the first time, a comprehensive review of the AI algorithms developed to investigate different issues related to nanofluids is conducted. The contributions presented in this paper reveal the high potential of AI methods as tools for the prediction and optimization in nanofluids. In addition, challenges and directions for future research in the area of employing AI techniques in nanofluids are introduced and discussed.
