The rapid growth of e-commerce in emerging markets presents a significant challenge: infrastructural disparities between urban and rural areas, raising issues of algorithmic fairness, equitable access, and digital inclusion. Despite extensive research in developed economies, the impact of infrastructural disparities on user perception of AI in emerging markets remains underexplored. This study introduced a user-centred, perception-based methodology to evaluate AI systems in digitally stratified contexts. A convergent mixed-method approach was employed, integrating quantitative surveys and a Delphi expert panel, grounded in four theoretical lenses: Adaptive Systems Theory, Sustainable AI, Behavioral Economics, and Diffusion of Innovation. The results revealed that urban users benefited from faster algorithmic responsiveness (e.g., real-time price updates) and more personalized recommendations. In contrast, rural users, while prioritizing cost-conscious and resource-efficient features, often experienced systemic delays and limited interface responsiveness. Notably, 68% of rural users reported repeated exposure to high-margin product recommendations, which suggests that the AI algorithms may be commercially optimized, favoring profitability over affordability. To address these inequities, region-specific solutions such as edge computing, inclusive design, and logistics optimization were proposed. These findings contribute a perceptual framework for assessing AI fairness and offer actionable pathways toward more inclusive and infrastructure-aware implementation of AI in emerging markets.