Nanotechnology is advancing in various fields such as medicine, material science and computer science. Machine learning (ML) strengthens nanotechnology by streamlining dataset analysis for the development and optimisation of nanomaterials’ behaviour. While both these technologies are still emerging, this chapter uncovers new frontiers that reveal their integrative transformation potential. The intricate and non-linear nature of nanofluids requires advanced predictive models in deriving useful applications. This chapter covers the use of ML models for predicting the physical properties of ionic liquids, such as viscosity, density, melting point and gas solubility. The selection of a nanofluid for a specific application highly depends on its thermophysical properties, the experimental determination of which is very costly, which leads to the introduction of the committee machine intelligent system (CMIS). It can also estimate changes in material formulation or operating parameters for overall heat transfer performance; furthermore, it can also analyse data to determine variables’ impact on efficiency. It provides a detailed overview of various artificial intelligence (AI) techniques that have been employed for the modelling, prediction and optimisation of different aspects of nanofluids, which have enormous significance in thermal engineering. It also discusses the different types of neural networks such as multilayer perceptron (MLP), radial basis function (RBF), generalised regression neural network (GRNN) and how they have been applied to model thermophysical properties like thermal conductivity, specific heat and viscosity as well as performance metrics like heat transfer coefficient and Nusselt number in heat exchangers particularly in renewable energy systems. It also explains the fuzzy logic approach for studying the thermal characteristics of nanofluids and optimising their performance. Artificial neural networks (ANNs), support vector regression (SVR), genetic algorithms and other AI algorithms are discussed here with their ability to handle large datasets and decipher complex patterns, making them highly effective in predicting thermodynamic physical properties of nanofluids. It also discusses about ensemble and modern algorithms, such as boosted regression techniques, K-means, K-nearest neighbour (KNN) and XGBoost, which can adapt to diverse data types, providing improved predictive performance. This chapter also explains the integration of computational fluid dynamics (CFD) and AI algorithms to overcome the problem of pressure drop caused by nanoparticles within the liquid and enhance nanofluid heat transfer in cooling systems. This chapter also demonstrates the high potential of AI methods as powerful tools for prediction, optimisation and decision-making in nanofluid research, which can significantly reduce the need for expensive and time-consuming experimental investigations.