A critical development between artificial intelligence (AI) and machine learning (ML) and nanotechnology enables contemporary innovation for ionic liquids, nanofluids, and bionanofluids investigation. The complex fluids have unique thermophysical characteristics that make them useful for different industrial fields, including electronics cooling, renewable energy systems, and biomedical engineering tools. The intricate and nonlinear behavioral characteristics of these substances make their prediction under different situations still difficult to understand. The combination of ML and AI provides adaptive predictive modeling solutions that enable new nanomaterials design through computational processes. Research studies currently show that AI methods effectively estimate nanofluid thermophysical properties. Artificial neural networks (ANNs) and support vector regression (SVR) successfully model thermal conductivity alongside viscosity properties of nanofluids better than traditional theoretical prediction models. Large datasets can be processed through these AI-driven methods to produce precise predictions about nanofluid behavior and establish more profound insights into their behavior patterns. The combination of ML and AI technologies with nanotechnology research on ionic liquids, nanofluids, and bionanofluids shows excellent potential for scientific progress. These technologies provide strong modeling capabilities that predict properties while generating new materials, which leads to industry-developing innovations and enhances sustainable applications in multiple sectors.