To address the growing concern regarding player safety and injury prevention in competitive sports, we present a synthetic dataset designed specifically for injury prediction. By leveraging Python libraries such as NumPy and Pandas, we aim to create a realistic representation of player health and injury data. Our synthetic dataset captures critical attributes such as player demographics, training intensities, recovery times, and previous injury histories. We establish correlations between these features and the likelihood of future injuries to accurately simulate real-world scenarios.