Computational Fluid Dynamics (CFD) simulations are essential for understanding and optimizing aerodynamic performance across various engineering applications, from aerospace to automotive design. However, high-fidelity CFD simulations are computationally expensive, requiring extensive time and resources to resolve turbulence and complex flow interactions accurately. This study proposes an AI-augmented turbulence and aerodynamic modeling framework that integrates Physics-Informed Neural Networks (PINNs) with traditional CFD solvers to accelerate high-fidelity simulations while maintaining accuracy [2]. By embedding fundamental fluid dynamics equations into deep learning architectures, our approach enables efficient turbulence modeling, reducing computational time without sacrificing precision. The framework leverages deep neural networks trained on high-resolution CFD data to predict turbulence dynamics and aerodynamic properties, thereby supplementing conventional turbulence models such as Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES). Our results demonstrate that the AI-augmented approach accelerates CFD simulations by up to 70%, significantly reducing computational costs while preserving high accuracy in key aerodynamic metrics such as drag coefficient, lift-to-drag ratio, and pressure distribution. Comparative analyses with traditional solvers confirm that our model successfully captures complex flow structures and turbulence interactions, validating its effectiveness in real-world aerodynamic applications. This study highlights the transformative potential of physics-informed AI in engineering simulations, bridging the gap between data-driven modeling and physics-based computation. The findings pave the way for the widespread adoption of AI-enhanced aerodynamic analysis, enabling real-time optimization and rapid prototyping in next-generation aerospace, automotive, and renewable energy systems.