This paper introduces “Slow AI,” a human-centered, iterative methodology designed to integrate artificial intelligence (AI) into marginalized communities interventions sustainably. Unlike conventional AI systems, Slow AI emphasizes gradual learning, community participation, and socio-cultural sensitivity. By engaging directly with local residents and stakeholders, we explore how a deliberate and mindful integration of AI technologies can support sustainable development and address these pressing issues. The research explores experimental methodologies for data collection, modeling, and insight generation within the Campana-Altamira community in Monterrey, Mexico, a region facing significant socio-environmental challenges. Our approach combines the Locales Framework, Social Set Analysis, and Liquid Neural Network to ensure that information sharing and delivery formats are tailored to the community’s social structures and specific contexts. The findings suggest that adopting a Slow AI framework fosters a deeper understanding of local contexts, promotes community empowerment, and mitigates the risks associated with rapid, one-size-fits-all AI deployments. This study also contributes to academic discourse by articulating the theoretical foundations of Slow AI, highlighting its potential to advance social equity and inclusion. While the framework is in its initial phase, it lays the groundwork for future iterations, emphasizing the importance of mutual learning and interdisciplinary collaboration in AI development for underserved communities.