The selection of Meta-Features can strongly influence the performance and accuracy of semi-supervised learning methods. In this paper, we analyze the role of different meta-features in enhancing the performance of semi-supervised learning models. We present experiments with benchmark data sets to determine which meta-features contribute most significantly to model accuracy and robustness. We propose an enhanced safeguard system for semi-supervised learning that leverages meta-features to predict the potential benefits of pseudo-labeling, with a focus on simultaneously reducing computational resource consumption and improving the overall performance of semi-supervised learning models. By determining when to train a second predictor, our system optimizes computational efficiency.