Chronic conditions such as diabetes, hypertension, and asthma are prevalent among urban poor populations in developing countries like India. Effective monitoring of these conditions is essential for improving patient outcomes and resource allocation. The study will involve a mixed-methods approach combining quantitative data collection through EHR systems with qualitative interviews to gather insights from users and healthcare providers. Statistical models will be used to assess the system's performance in terms of precision and recall for chronic condition monitoring. A preliminary analysis suggests that the AI model achieves an accuracy rate of approximately 92% in identifying relevant medical conditions, indicating a significant improvement over manual methods. The AI-enabled EHR system demonstrates promise as a tool for improving chronic condition management in urban poor populations. Further validation and integration with local healthcare infrastructures are recommended. Commencing pilot programmes in selected communities to refine the system, followed by wider deployment across India's urban poor areas where similar challenges exist. AI EHR System, Chronic Conditions Monitoring, Urban Poor, Gambia Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.