This study investigates the integration of artificial intelligence (AI) techniques specifically support vector regression (SVR) and genetic algorithms (GA) for optimizing the thermal performance of residential buildings in extreme cold climates, with a focus on the himalayan region. SVR is employed to model the nonlinear relationship between indoor-outdoor temperature differentials and heat loss through building envelopes, achieving a high predictive accuracy (R2 = 0.9915) and outperforming traditional linear regression (R2 = 0.9832). To enhance insulation performance, a genetic algorithm is utilized to optimize wall configurations by adjusting material properties and layer thickness based on thermal conductivity and resistance parameters. A case study of a masonry residential structure in kashmir, monitored during the winter season of 2023–24, demonstrated a 28.3% reduction in total heat loss and a substantial increase in wall thermal resistance from 1.80 to 7.60 m2·K/W through the AI-optimized design. The SVR model predictions were validated against EnergyPlus simulations calibrated with empirical field data, confirming a low prediction error (<3%). The proposed AI-driven framework enables intelligent material selection and performance-based envelope design, offering a robust, scalable solution for enhancing energy efficiency, indoor thermal comfort, and sustainability in thermally sensitive regions.