The demand for intelligent monitoring systems tailored to elderly living environments is rapidly increasing worldwide with population aging. Traditional acoustic scene monitoring systems that rely on cloud computing are limited by data transmission delays and privacy concerns. Hence, this study proposes an acoustic scene recognition system that integrates edge computing with deep learning to enable real-time monitoring of elderly individuals’ daily activities. The system consists of low-power edge devices equipped with multiple microphones, portable wearable components, and compact power modules, ensuring its seamless integration into the daily lives of the elderly. We developed four deep learning models—convolutional neural network, long short-term memory, bidirectional long short-term memory, and deep neural network—and used model quantization techniques to reduce the computational complexity and memory usage, thereby optimizing them to meet edge device constraints. The CNN model demonstrated superior performance compared to the other models, achieving 98.5% accuracy, an inference time of 2.4 ms, and low memory requirements (25.63 KB allocated for Flash and 5.15 KB for RAM). This architecture provides an efficient, reliable, and user-friendly solution for real-time acoustic scene monitoring in elderly care.