In recent years, the integration of machine learning (ML) into various engineering domains has revolutionized traditional practices, enhancing efficiency, accuracy, and automation. As industries strive to harness the power of data-driven decision-making, understanding the diverse strategies within ML becomes paramount. This review delves into the application of machine learning techniques across real-world engineering scenarios, offering insights into their effectiveness and challenges. Whether you're a seasoned engineer or a novice embarking on a Machine Learning Training Course, this exploration aims to illuminate the practical landscape of ML in engineering. Supervised Learning: Supervised learning forms the cornerstone of predictive modeling in engineering applications. By leveraging labeled datasets, algorithms can learn to map input features to desired outputs, enabling tasks such as regression and classification. In industrial settings, supervised learning finds application ...