Machine learning (ML) is currently employed in every industry, including engineering, medicine, and other fields. Deep learning approaches are built on artificial neural networks (ANNs) and with another type of network stimulated neural networks (SSNs) which is a substitution of the program machine learning. By emulating the structure and function of human brain networks, they aim to mimic the communication between biological neurons. Numerous trustworthy new methods for pattern identification, data analysis, and automation are made available by neural networks. Very fast progress speeds and the capacity to acquire a problem's solution from a set of samples are two of its noteworthy advantages. This study provides an overview of the deep neural network (DNN) which is a sub part of deep learning. There have been tried to clarify neural networks and the theory behind them in this review study. There is also discussed about the advantages and applicability area of neural networks.