Predictive modeling relies on the quality and availability of data, and inaccurate or incomplete data can impact its accuracy. Overfitting, where a model performs well on training data but fails to generalize to new data, is a common challenge. Additionally, predictive models may not provide clear explanations for their predictions, requiring transparency and interpretability considerations.