In this paper, we propose an AI-enhanced framework for LiDAR-based healthcare monitoring systems, introducing a novel approach to vital sign detection through contactless means. Traditional wearable methods, while effective, pose limitations in patient comfort and usability, particularly in intensive care units, neonatal cases, burn patients scenarios, or even children with autism. By leveraging Light Detection and Ranging (LiDAR) technology, our method achieves sub-centimeter precision in detecting micro-movements such as chest expansions and pulse fluctuations. We further integrate AI-driven signal processing algorithms, including deep learning-based peak detection and frequency extraction, to enhance the accuracy and reliability of vital sign monitoring (VSM). In addition, this work explores the role of airborne LiDAR integrated with ground filtering techniques, extended to remote or disaster-affected healthcare delivery. Results demonstrate that AI-augmented LiDAR-VSM systems can wirelessly transmit processed data to healthcare platforms, enabling real-time alerts, anomaly detection, and patient triage. This framework lays the foundation for next-generation intelligent health monitoring with broad implications for telemedicine, emergency care, and smart hospital ecosystems.