Current advancements in nanotechnology result from machine learning (ML) and artificial intelligence (AI) integration because they enable precise prediction capabilities, perform optimization operations, and support data-driven decision processes. The chapter analyzes how ML and AI power the advancement of ionic liquids (ILs), nanofluids, and bionanofluids throughout their design step and synthesis processes and their industrial applications. ILs function as adjustable solvents, while nanofluids and bionanofluids demonstrate exceptional thermal and rheological properties for energy, biomedical, and industrial applications. Optimizing these complex multivariate systems is costly and time-consuming with standard methods. ML and AI enable efficient predictions of thermophysical properties, stability enhancement, and functionality modification with minimal experimentation. Several advanced algorithms such as deep learning together with genetic algorithms and neural networks are used to model important characteristics of nanoparticle dispersion and heat transfer efficiency and biocompatibility. AI systems enable researchers to identify fresh IL-nanoparticle connection systems together with their precise molecular bond patterns. The adoption speed for research and industry depends on data-based approaches with combined modeling frameworks and predictive analytics which run in real time. AI system involvement with experimental data leads to substantial process improvements in developing future ILs, nanofluids, and bionanofluids fostering innovation in green chemistry, biomedicine, and sustainable energy operations. Nanotechnology currently experiences modification through AI and ML which is presented in this chapter alongside predictions about future developments.