Artificial Neural Networks (ANNs) have emerged as a cornerstone of modern artificial intelligence, drawing inspiration from the structure and function of the human brain. These networks, composed of interconnected processing units called neurons, are capable of modeling complex, non-linear relationships within data. Over the past few decades, ANNs have evolved from theoretical constructs into practical tools widely used across various fields of science, engineering, and technology. The growing computational power and availability of large datasets have significantly contributed to the success of ANNs in solving real-world problems. From image classification and natural language processing to medical diagnosis and autonomous systems, ANNs have demonstrated remarkable performance, often surpassing traditional machine learning methods. This review paper presents a comprehensive overview of artificial neural networks, with a particular focus on three fundamental aspects: network architectures, learning algorithms, and real-world applications. Various architectural models such as feedforward, convolutional, and recurrent networks are discussed alongside key training techniques, including backpropagation and optimization strategies. Furthermore, the paper explores diverse domains where ANNs have been successfully implemented, highlighting their transformative impact across industries. The review concludes with recent advancements, ongoing challenges, and prospective directions for future research in neural network development.

Keywords: Artificial Neural Networks, Deep Learning, Network Architectures, Learning Algorithms, Real-World Applications, Pattern Recognition