Medicine price transparency is, like, an important problem in data-driven healthcare analytics, because medicine affordability does not only depend on whether the product is available, but also on manufacturer, dosage form, generic composition, strength, and even that package-level variation thing. In this paper we propose MedPrice-XAI-BD, a framework that’s both explainable and uncertainty-aware, aimed at predicting medicine prices using structured medicine data from Bangladesh. The dataset has 21,714 medicine records with attributes such as brand name, generic name, dosage form, strength, manufacturer, package container, and package size details. Because the dataset itself does not provide a direct numerical price column, we build a preprocessing pipeline that extracts valid prices in Bangladeshi Taka from package-text fields, and that produces 21,636 usable price records. Then we test several regression models for price prediction, including Random Forest, XGBoost, LightGBM, and CatBoost. Overall results suggest that CatBoost performs best across the board, giving MAE = 172.75 BDT, RMSE = 2689.38 BDT, and R² = 0.368, while still coping well with heterogeneous categorical medicine features. Besides that, we also train a low-medium-high price category classifier, and it reaches 97.30% accuracy and 0.973 macro-F1. This indicates a good separability between the different price ranges. For interpretability, we use SHAP-based explainability to pinpoint what really drives the price, dosage form, package size, package container, generic name, strength, and manufacturer they come out as the major influencers. Finally, we apply split conformal prediction to quantify uncertainty, and we get 90.25% empirical coverage for a nominal 90% prediction interval. Overall, the proposed framework shows how explainable AI plus uncertainty-aware prediction can help medicine market analysis, affordability research, and more transparent pharmaceutical data analytics in Bangladesh.
