Abstract: To overcome the limitations of traditional methods for estimating the equity internal rate of return (IRR) in wind power projects—such as low computational efficiency, parameter redundancy, and limited dynamic responsiveness—this study proposes a sensitivity-driven dynamic quantitative model. Based on economic evaluation data from 65 onshore wind power projects (2023–2025) developed by a major power generation group, seven key influencing factors—including installed capacity, total investment, and long-term loan interest rate—were identified through partial correlation analysis. A multiple linear regression model was then constructed, demonstrating strong reliability with an adjusted R² of 0.809 and a residual standard error of 1.559. Validation with 10 independent test projects showed that the model’s predicted equity IRR values achieved a mean absolute deviation of only 0.25% compared with results from professional financial software. The proposed model effectively supports rapid and data-driven investment decision-making during the early planning stages of wind power projects.