Noise pollution, stemming from human activities and urban development, presents significant health hazards across all age demographics. It necessitates the implementation of measures such as establishing safe sound standards, imposing bans on unnecessary noise, and conducting public education campaigns for long-term mitigation and protection. In this context, our paper focuses on utilizing AutoKeras for AI tasks in model training, employing Node.js/Flask for server functionality, and integrating Vue.js for result visualization and verification. This comprehensive approach ensures the system’s robustness, scalability, and optimal user experience. We have leveraged pre-trained models and optimized code to enhance operational efficiency. Our future endeavors aim to incorporate advanced AI techniques and integrate edge computing and IoT capabilities, ensuring adaptability and alignment with emerging technological trends. Notably, our model demonstrates a 5% increase in accuracy compared to previous iterations, underscoring our commitment to continuous improvement and collaborative efforts to advance the AI framework while upholding user-centric enhancements and ethical AI practices.