In regions where climate data is scarce, adapting to climate change becomes a significant challenge due to the lack of reliable information. This project addresses this issue by using Artificial Intelligence (AI) techniques to generate comprehensive climate datasets in a data-scarce region of Choke Mountain Watersheds in Ethiopia. The primary objectives are to fill gaps in existing in-situ precipitation and temperature observations and to create data for areas that are currently unmonitored. By applying advanced machine learning algorithms, we will improve the accuracy and reliability of climate data, and fill gaps in current datasets to ensure completeness. Ensuring the availability of a continuous dataset is crucial for informed decision-making in climate change adaptation.