AI Agent for Socioeconomic Prediction: Modeling Female-Headed Households in South Africa

Accurate socioeconomic prediction is critical for data-driven policymaking in regions facing systemic inequality. This paper introduces an AI agent-based framework to forecast the prevalence of female-headed households below the poverty line in South Africa, a demographic often disproportionately impacted by limited access to education, employment, and financial services. Targeting this group provides a focused lens on economic disparity. The proposed system integrates ensemble learning with adaptive model refinement, combining gradient boosting, neural networks, and random forests within a stacked ensemble. A reinforcement learning module dynamically adjusts model weights based on new socioeconomic data, improving generalization. SHAP-based explainability is incorporated to interpret predictions, highlighting key drivers such as income, education, and welfare access. Experimental results on realworld datasets show that the AI agent achieves a lower Mean Squared Error (MSE) and standard deviation, outperforming baseline regressors in both accuracy and stability. The framework is scalable and future-ready, with ongoing work focused on integrating real-time prediction, multimodal data sources, and federated learning for privacy-preserving, cross-institutional collaboration. This research demonstrates the potential of adaptive AI systems to support targeted interventions and equitable policy formulation in dynamic socioeconomic environments.