<p>Ternary hybrid nanofluids (THNFs), made of three different nanoparticles in a base fluid, are emerging as advanced heat transfer fluids with great potential for thermal management. Compared to mono and binary nanofluids, THNFs exhibit superior thermal conductivity, stability, and viscosity control, making them suitable for applications in electronics cooling, renewable energy, automotive engines, and biomedical devices. However, challenges persist in optimizing their synthesis, ensuring long-term stability, and accurately evaluating their thermophysical properties under various conditions. Recently, machine learning (ML) models have been utilized to predict and enhance THNF performance, improving accuracy and reducing experimental costs. This review explores THNF synthesis methods, thermal property analysis, and practical applications, while assessing the role of ML in predictive modeling. It also addresses key challenges and provides future directions for developing stable, scalable, and application-ready THNFs. The strength of this work lies in extracting insights from experimental data and computational intelligence, offering a comprehensive framework for researchers and engineers to accelerate THNF implementation in next-generation thermal systems.</p>