Machine learning (ML) is a branch of artificial intelligence that lets computers learn from data, find patterns, and make decisions without being explicitly programmed. Machine learning has become increasingly popular in various industries in recent years due to its ability to find insights and opportunities in big data. This study gives an in-depth look at machine learning algorithms and how they are used in industrial problems. The study shows how important it is to choose the right algorithm for a given problem and how important it is to use the right techniques for preprocessing and evaluating data. Different ML models were talked about that can predict machine maintenance, sales based on advertising, backorders, fraud detection, and the demand for certain products. The study also discusses the limitations of the study and suggests future research directions, such as exploring the application of deep learning algorithms and using other performance metrics. This study is a great resource for researchers, practitioners, and students who want to learn about the basics of machine learning (ML) and how it is used in industry.