Persistent disparities in science, technology, engineering, and mathematics (STEM) education continue to marginalize underserved populations across low-income, rural, and minority communities, limiting participation in knowledge economies and widening socio-economic inequalities. Recent advances in artificial intelligence (AI) present transformative opportunities to address these gaps through adaptive, scalable, and context-aware learning systems. This study provides a cross-context analysis of AI-driven models deployed within community-based interventions, examining their effectiveness in enhancing access, engagement, and learning outcomes in diverse educational settings. Drawing on comparative case studies from community learning hubs, informal education programs, and digitally mediated outreach initiatives, the analysis evaluates machine learning–enabled personalized tutoring, natural language processing–based learning assistants, and predictive analytics for early identification of learning risks. Findings indicate that AI-driven interventions significantly improve learner retention, conceptual understanding, and digital literacy when integrated with culturally responsive pedagogies and local stakeholder engagement. However, disparities in infrastructure, data bias, and ethical concerns remain critical barriers to equitable implementation. The study concludes by proposing a hybrid framework that combines AI capabilities with community-led strategies to ensure inclusivity, transparency, and sustainability. By aligning technological innovation with grassroots educational ecosystems, AI-driven models can play a pivotal role in bridging STEM education gaps globally across diverse global contexts.