Abstract: Helicopter rotor noise remains a critical barrier to the wider adoption of rotary-wing aircraft. This study aims to identify and quantify the principal aerodynamic noise sources of helicopter rotors, evaluate state-of-the-art high-fidelity prediction techniques for coupled flow-acoustic fields, and develop an integrated active control framework capable of delivering robust noise reduction across multiple flight regimes. First, the research systematically examines four dominant noise mechanisms: thickness noise generated by the unsteady displacement of rotor blades, loading noise arising from time-varying aerodynamic forces, blade/vortex interaction (BVI) noise caused by shed vortices impinging on subsequent blades, and high-speed impulsive (HSI) noise associated with transonic flow on advancing blades. Each mechanism is characterized in terms of spectral content, directivity pattern, and sensitivity to rotor parameters such as advance ratio, collective pitch, and tip Mach number. Next, the evolution of prediction methodologies is reviewed, including Reynolds-averaged Navier-Stokes (RANS) solvers coupled with acoustic analogies, hybrid large-eddy simulation (LES) approaches, and fully coupled computational aeroacoustics (CAA) frameworks. The comparative strengths and limitations of these methods are highlighted, with particular attention to accuracy in capturing unsteady flow features and computational costs. Building on this foundation, the study introduces a classification of active noise control (ANC) schemes according to their deployment mode. Two principal categories are defined: (a) onboard platform-based systems, which integrate sensors and actuators directly on the rotor hub or blade surfaces, and (b) ground-assisted approaches employing fixed or mobile ground stations to generate counter-noise fields or adaptive inflow conditions. Each category is assessed regarding the noise-suppression efficiency, bandwidth of operation, power requirements, and feasibility of retrofitting onto existing airframes. To address the multi-objective nature of rotorcraft performance, an active aerodynamic noise control strategy is proposed. This approach synergizes active and passive techniques, implements adaptive multi-objective optimization, and leverages interdisciplinary integration. Specifically, adaptive trailing-edge flaps and trailing-edge serrations are combined with real-time blade pitch modulation to extend the control bandwidth, a multi-objective controller simultaneously minimizes sound pressure levels, fuel consumption, and vibratory loads, and a digital-twin environment fuses real-time flight data with machine-learning algorithms to refine control laws on-the-fly. High-fidelity numerical simulations validated by wind-tunnel experiments demonstrate that the proposed framework achieves up to 8 dB overall sound level reduction in BVI-dominated flight regimes without compromising lift or increasing power draw. Quantitative results indicate a 15 % improvement in acoustic efficiency relative to standalone passive measures and a 10 % reduction in vibratory loads. Finally, the study identifies key avenues for future research: the development of improved acoustic source models that capture nonlinear blade-vortex interactions, optimization of distributed sensor and actuator networks via information-theoretic metrics, and advancement of intelligent control algorithms capable of learning complex flow-noise correlations. Integration with big-data analytics, artificial-intelligence-driven prognostics, and novel lightweight composite materials is also recommended to facilitate real-world implementation. Collectively, these contributions furnish a comprehensive theoretical and technological roadmap for achieving full-condition low-noise helicopter flight.