Healthcare environment monitoring requires progressively advanced solutions for protecting patients and sustaining excellent care environments. The study adopts a proof-of-concept approach that employs the technology of deep learning to integrate several sensor perceptions by utilizing a duly optimized CNN framework to examine healthcare environmental data. This research uses five critical environmental parameters such as temperature, relative humidity, PM2.5, TVOC and HCHO to develop monitoring technologies by means of pure experimental research and architectural enhancements. The system that has been developed achieves 89.5% of validation accuracy and a 65% decrease in response time of the system to environmental changes and 82% decrease in false alarms. The study establishes that deep learning systems can drive strong environmental monitoring infrastructures in high-risk medical centers that can be used as foundation points in better proactive surveillance systems than threshold-based systems.