EOS Data Analytics aimed to enhance cotton yield predictions in Texas, a major cotton-producing region increasingly affected by climate volatility. By leveraging AI-powered analytics, the team developed a custom model to estimate crop yields across five target counties. The methodology integrated historical satellite imagery, crop yield statistics, detailed weather data, and soil metrics, resulting in an AI model tailored to the distinct environmental conditions of each area. At the core of the solution was a Random Forest Regression model designed to forecast cotton yields under varying scenarios. This enabled farmers to make informed decisions on planting schedules and water management strategies.