This project, undertaken by EOS Data Analytics, focused on enhancing cotton yield predictions in Texas, a major cotton-producing region experiencing increasing climate volatility. Utilizing AI-powered analytics, EOSDA developed a custom model to estimate crop yields in five target counties. The approach combined historical satellite data, crop yield statistics, detailed weather information, and soil moisture data, enabling the creation of an AI model tailored to the unique environmental conditions of each region. With the Random Forest Regression model as its core, the project aimed to predict cotton yields in various scenarios, supporting farmers in selecting optimal planting schedules and water management strategies. This advanced forecasting solution helps address climate impacts on cotton yield, allowing farmers to take proactive measures to reduce risks and maximize crop productivity.