As Urban Heat Island (UHI) effects intensify, mitigating street-level heat has become a critical challenge for urban resilience. However, current research relies mostly on horizontal satellite data, ignoring the thermal impact of vertical surface materials, while most design tools are either too slow for urban-scale analysis or incapable of simulating vertical surfaces. Consequently, the thermal impact of vertical envelopes remains unmapped, leaving planners without an accessible workflow to evaluate material choices during early design phases.  This project presents Algorithmic Cooling, an AI-driven urban data processing pipeline and a hermal analytic portal developed to bridge this data gap. Our methodology employs computer vision and vision-language models (VLMs) to automatically recognize facade materials and map them to spatial geometries using uncurated street-level imagery. This multi-stage filtering and segmentation process structures pixel-level data into a granular, 3D city-scale material database. The extracted data feeds a machine learning surrogate model, trained on robust environmental simulations, to instantly predict hourly surface temperatures and thermal radiation flux within street canyons.  The deployed web portal couples these spatial databases directly with the surrogate engine, transforming abstract thermal calculations into intuitive decision-support analytics. Through interactive 3D visualizations, users can evaluate localized heat loads, modify facade material specifications, and immediately assess microclimatic impacts. Together, Algorithmic Cooling establishes an operational, scalable framework for integrating thermodynamic performance into climate-responsive urban design and planning practice.