Climate-sensitive illnesses like malaria and climate-related fever largely rely on climatic factors like temperature, humidity, and rainfall. Classical models are unlikely to generalize over different geographies and climatic conditions and need more interpretable and robust solutions. In this paper, a new AI predictive model has been introduced by grounding it in a stacking ensemble of regressors enriched with explainable AI (XAI) methods which basically aggregates many base learners into a meta-learner and uses SHAP and LIME to effect global and local interpretability of predictions. Tested on 1,456 climate-health instances, the proposed model outperformed individual models (R2 = 0.9489, MAE = 7.41), effecting better predictive accuracy and transparent decision reasoning, making it more reliable and actionable for practitioners.