Background: Rural infrastructure in areas such as Osmanabad district continues to face challenges of climatic stress, resource scarcity, and slow design processes. Conventional engineering practices are typically not flexible and not fast enough to enable widely deployment of resilient technologies. Recent strides in AI, in particular generative design and reinforcement learning, could provide fruitful paths for faster design iteration and performance improvement in these types of contexts. Objectives: The objective of this work is to investigate the potential for AI-based design acceleration to support increased thermal and structural efficiency, shorten iteration times, and encourage community-aligned infrastructure. The discussion ranges from microchannel heat sinks to self-healing concrete to modular housing elements designed for Osmanabad’s semi-arid climate. Methods: We employed a hybrid modelling strategy that combined computational modelling, experimental validation, and participatory validation. Using Python-based frameworks and ANSYS Fluent, AI methods, including topology optimization, GANs, and reinforcement learning, have been implemented. Field trials were undertaken at two pilot sites with stakeholder workshops and theme feedback analysis. Results: AI-optimized workflows achieved a 42–58% reduction in design iteration time and up to 35% enhancement in thermal resistance. Experimental prototypes showed better resistance to fatigue and healing performance. Users preferred AI-generated layouts compared to traditional designs. Major stakeholder feedback received – better usability and cultural fit of AI-generated layouts: 66% stakeholder preference for AI-generated designs. Intuitive community involvement was enhanced by visual tools such as heat maps and 3D printing. Conclusion: The results support that AI is an enabler in the development of a rural, affordable, and resilient infrastructure system in India. With the integration of ethical guards and channels for participation, the response becomes more human and ready to work in regional realities. The need for future work about edge-based AI deployment and longitudinal performance tracking across seasonal cycles is also discussed.