Self-Supervised Learning (SSL) has emerged as a significant advancement in deep learning, offering efficient ways to train neural networks using large volumes of unlabeled data. This review explores the practical applications and benefits of SSL in industrial and medical contexts, emphasizing its ability to enhance prediction and classification tasks without relying on traditional annotated data. SSL addresses challenges such as the high cost, time, and effort required for data annotation, making it suitable for scenarios where labeled data is scarce or expensive to obtain. The review highlights the effectiveness of SSL in various tasks, including fault prediction in machinery, disease diagnosis, and anomaly detection in medical imaging. SSL models, such as generative, predictive, contrastive, and non-contrastive approaches, are discussed, along with their applications in medical data classification and engineering contexts. The review also addresses the limitations of SSL and suggests future research directions. Overall, SSL provides a flexible and efficient solution for data-driven decision-making in both industrial and medical fields, contributing to more accurate and reliable predictions.Self-Supervised Learning (SSL) has emerged as a significant advancement in deep learning, offering efficient ways to train neural networks using large volumes of unlabeled data. This review explores the practical applications and benefits of SSL in industrial and medical contexts, emphasizing its ability to enhance prediction and classification tasks without relying on traditional annotated data. SSL addresses challenges such as the high cost, time, and effort required for data annotation, making it suitable for scenarios where labeled data is scarce or expensive to obtain. The review highlights the effectiveness of SSL in various tasks, including fault prediction in machinery, disease diagnosis, and anomaly detection in medical imaging. SSL models, such as generative, predictive, contrastive, and non-contrastive approaches, are discussed, along with their applications in medical data classification and engineering contexts. The review also addresses the limitations of SSL and suggests future research directions. Overall, SSL provides a flexible and efficient solution for data-driven decision-making in both industrial and medical fields, contributing to more accurate and reliable predictions.