Semi-supervised learning (SSL) combines labeled and unlabeled data to improve the accuracy of machine learning models, allowing them to learn from both scarce and abundant data sources. Unlike supervised learning, which relies on large amounts of labeled data, SSL uses a small portion of labeled data to train models and treats the rest as test data, reducing costs associated with labeling. In contrast to unsupervised learning, which does not require labeled data, SSL provides better learning results by using both labeled and unlabeled data. The technique offers benefits in areas such as speech recognition, audio classification, and web classification, where large amounts of unlabeled data are available but labeled data is scarce or expensive to acquire. By leveraging SSL, machine learning models can achieve improved accuracy and cost-effectiveness while handling challenges associated with missing labels in datasets.