Machine learning contributes to predictive modeling by employing algorithms that analyze historical data to identify patterns and make forecasts. This process involves the use of techniques like regression, classification, and clustering to build models capable of predicting future outcomes based on input variables. As these models are trained with large datasets, machine learning continuously improves their accuracy over time, enabling businesses to make informed decisions. Applications of predictive modeling range from financial forecasting to customer behavior analysis, enhancing organizational effectiveness.