With the advancement of artificial intelligence technology, baby monitoring systems have become increasingly prevalent in daily life. This paper presents an AI-based infant behavior monitoring system that utilizes computer vision techniques and deep learning algorithms, integrated with hardware components such as the Raspberry Pi 4B and Camera V2, to achieve real-time monitoring and intelligent analysis of infant behavior. The system employs the Google MediaPipe pose recognition algorithm to extract infant joint features within predefined safety zones and uses an optimized Moondream 2 model for multimodal data inference, significantly enhancing the system′s real-time responsiveness and accuracy. Additionally, the system incorporates a lightweight time-series analysis module to improve sensitivity to behavioral changes and integrates dynamic alert functions to ensure efficient and reliable monitoring. By leveraging the Home Assistant platform, MQTT protocol, and network tunneling technology, the system supports remote access and real-time notification capabilities. Experimental results demonstrate excellent performance in terms of accuracy and stability, making the system widely applicable in home monitoring and intelligent caregiving scenarios, and providing a novel solution for the safety management of infants and young children.