<p>Developing materials with superior acoustic and vibrational damping properties is essential for addressing challenges in industrial applications for noise and vibration control. This study focuses on the modeling and optimization of acoustic and vibrational performance in multi-walled carbon nanotube (MWCNT)-reinforced polymer composites by enhancing damping efficiency and optimizing MWCNT concentration, dispersion quality, and polymer matrix characteristics, significantly influencing the material’s dynamic behavior. A dynamic response model of the polymer nanocomposites was developed by coding in MATLAB environment followed by Teaching–Learning-Based Optimization (TLBO) algorithm to optimize the critical parameters. The optimization process effectively identified the ideal MWCNT content and dispersion to maximize damping performance while maintaining the structural integrity of the composite material. The results demonstrate a significant improvement in the noise and vibration attenuation capabilities of optimized MWCNT composites with coefficient of determination (R2 = 0.98), root mean square error (RMSE = 0.012), and mean absolute percentage error (MAPE = 1.831%), The TLBO algorithm provides a computationally efficient and reliable tool for achieving optimal performance configurations by the developed framework not only predicting its acoustic and vibrational behavior with high accuracy but also provides valuable insights into designing high-performance materials.</p>