For women entrepreneurs in rural areas, getting affordable credit is still a big problem because they have short credit histories, can't use collateral, and rely on informal financial practices. This study suggests an AI-based credit scoring framework aimed at bridging these gaps through the utilization of alternative data, fairness-aware optimization, and explainable machine learning methodologies. The framework uses demographic, financial, and behavioral features to balance predictive accuracy with subgroup equity by using gradient boosting with monotonic constraints and fairness-penalized loss functions. Isotonic regression for probability calibration makes sure that probability estimates are accurate, and SHAP-based narratives that give clear borrower-level reasons for decisions make things easier to understand. The proposed framework is better than logistic regression and baseline gradient boosting models, as shown by experimental results that show higher AUC, better calibration, and fewer differences between subgroups. Stability analysis verified resilience to drift, whereas portfolio risk assessment demonstrated tangible trade-offs between acceptance and anticipated loss. The results show that the framework is both technically sound and socially responsible, making it a good way for microfinance institutions and lenders to make credit more available.