Artificial intelligence (AI) is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies, in the new generation of big data and Industry 4.0. In particular, machine learning (ML) becomes increasingly more frequently applicable in manufacturing applications. This chapter presents a systematic overview of today’s applications of ML techniques and approaches for their usage in the manufacturing environment. The utilization of ML methods is related to manufacturing process planning and control, predictive maintenance, quality control, in situ process control and optimization, logistics, robotics, assistance and learning systems for shopfloor employees. Exhaustive fundamental and problem concept describes how to select an appropriate ML approach including the combination of multiple approaches. Supervised methods dominate the state of the art with reinforcement learning methods gaining more attention in recent years. Subsequently, the gains of ML such as derivation of model configuration based on the data, generation of behavioral models through training, easy validation of the model and optimization of the real system based on the model are illustrated. This illustration is surrounded by two use cases from plant engineering. Data analysis points out how these concepts can be analyzed and with behavior model can be achieved regarding the implemented ML method. Finally, conclusions and outlook reflect the benefits and drawbacks of ML and draw the way for future development.