This paper reviews the advancements and practical applications of Self-Supervised Learning (SSL) in deep learning (DL) contexts, particularly in industrial and medical fields. SSL is a technique that uses large volumes of unlabeled data to train neural networks, addressing the limitations of traditional machine learning (ML) and DL models in handling unlabeled data. The review highlights the benefits of SSL, such as reducing the need for extensive data annotation, saving time and computational resources, and improving prediction accuracy and reliability. The paper is structured into several sections, including an introduction, materials and methods, related work, and practical applications. It discusses the key characteristics and workabilities of SSL, including generative, predictive, contrastive, and non-contrastive models. The review also explores the practical applications of SSL in medical data classification, engineering contexts, patch localization, context-aware pixel prediction, natural language processing, and auto-regressive language modeling. Key findings include the effectiveness of SSL in improving diagnostic accuracy in medical imaging, enhancing fault prediction in industrial systems, and enabling efficient data classification in various domains. The paper concludes by emphasizing the potential of SSL to support industrial engineers and medical professionals in making precise predictions and diagnoses with reduced computational costs and increased efficiency.This paper reviews the advancements and practical applications of Self-Supervised Learning (SSL) in deep learning (DL) contexts, particularly in industrial and medical fields. SSL is a technique that uses large volumes of unlabeled data to train neural networks, addressing the limitations of traditional machine learning (ML) and DL models in handling unlabeled data. The review highlights the benefits of SSL, such as reducing the need for extensive data annotation, saving time and computational resources, and improving prediction accuracy and reliability. The paper is structured into several sections, including an introduction, materials and methods, related work, and practical applications. It discusses the key characteristics and workabilities of SSL, including generative, predictive, contrastive, and non-contrastive models. The review also explores the practical applications of SSL in medical data classification, engineering contexts, patch localization, context-aware pixel prediction, natural language processing, and auto-regressive language modeling. Key findings include the effectiveness of SSL in improving diagnostic accuracy in medical imaging, enhancing fault prediction in industrial systems, and enabling efficient data classification in various domains. The paper concludes by emphasizing the potential of SSL to support industrial engineers and medical professionals in making precise predictions and diagnoses with reduced computational costs and increased efficiency.