Turbulence remains one of the most persistent and unresolved challenges in classical physics, where long-term prediction is constrained by chaotic instabilities and the limitations of statistical approaches. Despite decades of research, a fully predictive theory of turbulence is still lacking. In this article, I introduce a novel topological interpretation of turbulence through the lens of knot theory. Vortex lines in a turbulent flow are modeled as topological curves, allowing their entanglement structures to be classified using knot invariants such as the Jones polynomial and the linking number. I hypothesize that certain invariants remain conserved or slowly varying across turbulent regimes, even when local velocity fields evolve chaotically. These invariants may therefore serve as predictive markers that capture hidden order within turbulence. To explore this idea, I perform numerical simulations of the three-dimensional Navier–Stokes equations, from which vortex filaments are extracted and analyzed. The resulting topological structures are then classified according to their knot type, and their invariants are computed to test their robustness under turbulent evolution. The findings suggest that turbulence may not be entirely random but instead carries topological signatures that persist across scales. Such an approach has the potential to transform the way turbulence is understood and modeled. Finally, I discuss the possible applications of this framework in industrial fluid dynamics, aerospace engineering, and turbulence control, highlighting how topological invariants could provide a pathway toward a predictive theory of turbulence.