This research project presents the development of an infant smart monitoring system using multiple non-invasive sensors to detect various physiological functions. The system can evaluate different physiological activities such as respiration, movement, noise, position, ambient temperature, and humidity. By processing the acquired data from different sensor modules, the system can generate alarm signals for adverse situations such as the occurrence of apnea, seizure, or noisy and uncomfortable environmental conditions. The system will also detect critical respiratory conditions by analyzing breathing data and blood oxygen level (SpO2) using machine learning (ML) models such as neural networks. The proposed system allows the caregiver to monitor the patient’s condition remotely by implementing wireless communication with a remote computer or a cell phone.