<p>Downy mildew is a devastating disease in sorghum that causes yield losses of up to 65%. Constructing prediction models will help optimize management practices, ensuring timely implementation to minimize crop losses. This study screened for disease severity and yield loss for six sorghum cultivars viz., CO 4, CO 5, CO 30, K 12, DMS 652, and TNR 12 exhibiting moderately resistant to moderately susceptible) during three growing seasons from 2018 to 2021. Consequently, stepwise regression models were used to analyze the correlations of weather factors (maximum and minimum temperatures; morning and evening relative humidity, sunshine hours, and rainfall) with downy mildew severity and yield loss (%). For each sorghum cultivar disease predictive models were developed for downy mildew severity and yield loss prediction. The results revealed that the evening temperature, morning relative humidity, and bright sunshine hours with rainfall observed one week ahead were conducive to downy mildew in sorghum and yielded positive correlation coefficients (r) ranging from 0.684 to 0.864. Except for maximum temperature and evening RH, these weather factors were positively associated with downy mildew severity and yield loss, with the highest coefficients of determination (R²) ranging from 71.35 to 93.86 and 70.75 to 91.22%, respectively. From 2021 to 2022 and 2022 to 2023, the models for each cultivar with the highest R² values were validated. The validation results revealed a 86.8–88.9% variability, indicating a highly accurate prediction of disease severity and yield loss for the cultivars tested. Farmers can use this tool to forecast disease outbreaks and guide disease management decisions.</p>