Diabetes prevalence is high in rural areas of Ethiopia, where access to healthcare services is limited. Participants were randomly selected from three rural villages. An AI model was trained on a dataset including demographic information, lifestyle habits, and blood glucose levels. The AI model achieved an accuracy rate of 82% in identifying diabetic patients with a standard deviation of ±5%, indicating moderate precision. The system demonstrated promising preliminary efficacy but requires further validation and refinement before implementation. Further studies should explore the long-term reliability, cost-effectiveness, and user-friendliness of the AI model in rural settings. AI-based screening, diabetes detection, rural Ethiopia, precision medicine Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.