Climate change has intensified the frequency and severity of droughts, significantly impacting water resources, agriculture, and ecosystems. Traditional drought indicators typically focus on recent conditions rather than future projections, and conventional forecasting methods often struggle to capture the complex, non-linear relationships between long-term climate variables and droughts. This project aims to fill this gap by developing a machine-learning model to project drought conditions in Iowa, specifically focusing on the U.S. Drought Monitor categories. The developed model, a long short-term memory, was rigorously validated to ensure reliability and accuracy. With a Root Mean Squared Error of 0.19 and an R² Score of 91%, the model achieved a high level of accuracy, making it effective in guiding conservation practices and enabling timely interventions. The model was trained on historical data from 2012 to 2019 and thoroughly evaluated using out-of-sample data from 2002 to 2011. It exhibited strong performance in the projection of drought conditions across Iowa’s Hydrologic Unit Code 08 watersheds. The model predicted drought conditions for 2030–2050 using three climate models: MPI-ESM1-2-HR, BCC-CSM2-MR, and CNRM-ESM2-1. Results indicate that droughts in the coming decades will become more intense, prolonged, and frequent, with projections suggesting intensities up to twice as severe and durations and frequencies in northwestern regions up to nine times higher than historical records. Moreover, this research developed an interactive application for visualizing future drought conditions in Iowa. This tool aids users in making informed water management decisions by providing stakeholders with detailed visualizations and technical information.