The process sector generates a lot of data from various sources, such as sensors, manufacturing systems, and supply chains. Utilizing analytics and data mining techniques, it is possible to draw useful insights and trends from this data, leading to better decision-making and process optimization. Machine learning, which enables the development of predictive models and automated decision-making processes, is primarily reliant on data mining and analytics. This work investigates how machine learning is applied to analytics and data mining in the process sector. The concept of data mining and analytics is then discussed, it starts off by providing a broad making decision sector and the data management and analytics issues it faces