The primary objective of the Artificial Neural Network (ANN) is to build useful ‘computers' for serious challenges and to reconstruct intelligent data methodological approaches like pattern recognition, classification, and generalization through using simple, distributed , and robust processing units known as artificial neurons. ANNs are parallel implementations of non-linear static-dynamic systems that are fine-grained. The considerable degree of interconnectivity that provides neurons their great computing capacity through their vast parallel -distributed structure gives ANNs their intelligence and ability to tackle difficult issues. The recent spike in demand in ANN is primarily due to the fact that ANN algorithms and architectures may be deployed in real-time applications using VLSI technology. The scope of ANN applicatio ns has exploded in recent years, fueled by both theoretical and practical accomplishments across a wide range of fields. The theory, models, and applications of artificial neural networks are briefly discussed.