Purpose <p> To provide a comprehensive and future-focused synthesis of aerodynamic noise reduction techniques by introducing a novel classification framework that bridges traditional active control systems with emerging machine learning-based predictive approaches. The review aims to deliver actionable insights for advancing intelligent and energy-efficient aeroacoustic control solutions.</p> Methods <p> A systematic evaluation and categorization of active noise control strategies, including plasma actuators, smart materials, and adaptive algorithms such as reinforcement learning, neural networks, and filtered-xLMS systems. Comparative analysis is performed using quantitative performance metrics including decibel (dB) reduction, computational complexity, feasibility, and implementation readiness.</p> Results <p> The review highlights the strengths, limitations, and performance trade-offs of both conventional and AI-driven noise reduction technologies. New hybrid and adaptive approaches demonstrate promising improvements in noise attenuation, operational efficiency, and system intelligence, positioning them as viable candidates for next-generation aeroacoustic control.</p> Conclusion <p> This study establishes a forward-looking roadmap for innovative noise control research by integrating advanced data-driven methods with established active control techniques. The structured comparative framework and identification of hybrid strategies make the review a unique and valuable contribution toward the development of sustainable, adaptive, and practical aerodynamic noise mitigation systems.</p>