Artificial neural networks are computational models inspired by the structure and functionality of the human brain, designed to identify complex patterns and relationships within data. Comprising interconnected nodes or neurons, artificial neural networks process input data by applying weights and biases, followed by activation functions that determine whether a neuron ‘fires’ to influence subsequent layers. Through iterative training processes such as backpropagation and the adjustment of weights and biases over multiple epochs, artificial neural networks learn to minimize prediction errors and improve performance. The architecture of a neural network including the number of layers and neurons plays a critical role in its effectiveness, with improper configurations leading to overfitting or underfitting. The success of an artificial neural network also depends heavily on the quality and volume of the training data. A well-structured neural network, trained on clean and extensive data sets, can produce highly accurate predictions, making artificial neural networks a foundational tool in modern artificial intelligence and machine learning.