The term "machine learning model" in predictive analysis involves algorithms that use historical data to make predictions about future outcomes. These models are trained on specific datasets to identify patterns and relationships within the data. Common approaches include regression, classification, and clustering. Once a model is trained, it can be used to predict trends, classify new data, or identify anomalies. Regular updates and retraining are often necessary to maintain accuracy, especially as new data becomes available, enhancing the model's predictive capabilities.