Extreme heat events, intensified by climate change, threaten human health and urban sustainability. In this session I will introduces an integrated dataset and hybrid modeling framework to enhance spatial AI applications for human thermal comfort and heat resilience. Using the Texas A&M University campus as a testbed, it combines high-resolution 3D urban models, meteorological data, and physics-based simulations of the Universal Thermal Climate Index (UTCI) at a 1-meter resolution. The Spatiotemporal Vision Transformer (ST-ViT) model integrates multimodal inputs to provide real-time heat stress predictions while maintaining atmospheric dynamics. Implemented in an interactive digital twin platform, this approach supports applications like dynamic heat mapping, route optimization, and mitigation planning, offering a scalable framework for urban climate resilience.