The weighted solution path algorithm of support vector regression helps improve prediction accuracy and generalization of regression models by adjusting the penalty for errors based on the presence of abnormal data. By assigning different weights to error penalties for abnormal samples, the algorithm reduces their negative impact on the model. This approach allows for better handling of noisy data with varying variances, leading to more accurate predictions and improved model performance.