The integration of machine learning (ML) and artificial intelligence (AI) with nanotechnology, particularly involving ionic liquids, nanofluids, and bionanofluids, is revolutionizing materials science and engineering. This fusion enables improved control over the material characteristics and process optimization, leading to the development of advanced materials and effective thermal management solutions with wide-ranging applications. ML techniques, such as artificial neural networks (ANN) and the support vector regression (SVR), play a vital role in predicting the thermophysical properties of nanofluids, enhancing the heat transfer in systems such as heat exchangers and reducing energy usage. Additionally, AI algorithms link the reaction conditions to nanoparticle attributes, which leads to higher production yields and shorter synthesis times. AI’s capacity to forecast nanoparticle toxicity, utilizing models like decision trees and random forests to assess the physicochemical properties—including size, shape, and the surface charge—is crucial for crafting safer nanomaterials. Moreover, the AI optimizes nanoparticle synthesis through improved photochemical techniques, boosting the precision and the scalability while streamlining the drug discovery and formulation processes, which supports personalized treatments and more effective clinical trials. Predictive models like multilayer perceptron (MLP) and radial basis function neural networks (RBF-ANNs) are successfully employed to anticipate thermophysical properties, such as viscosity and thermal conductivity, providing essential real-time predictions for industrial applications. Despite significant progress, the challenges remain regarding data quality and interpretability at the AI-nanotechnology nexus.