The emergence of AI as a revolutionary technology has been based on deploying neural networks (NNs) and deep learning techniques, which are considered the pillars of its functioning. Through the design of the human brain's structure and function, the NNs received an opportunity to execute tasks in an efficient way, for example, image recognition, natural language processing and decision-making. This paper studies the work of neural networks by predictive threat modeling in cybersecurity. It explains how a neural network is trained with the past cyber-incidence data to become the system that detects the patterns of current events and forecasts the future breaches and attacks. The AI service as a revolutionary technology, including NNs and deep learning approach, which are considered as its architecture can be called the key elements of the service. Through the linking of evolution and formation of a functional human brain, the NNs were indeed provided a platform for a smoothly running process which included the recognition of images, natural language processing and decision making. This essay will be directly targeted on the concept of brain-like networks and their use as a cyber security predictive model. Several neural network architecture variations, for example, feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), which have unique capabilities and tasks, are demonstrated. After introducing the idea of learning in NNs, the paper thoroughly explains methods that let NNs get better with historical data—gradient descent and backpropagation. It then goes into detail about the critical role that training data plays and the training process itself, including issues like overfitting. Subsequently, we present the wide range of practical uses for NNs in several domains, illustrating how they have revolutionized computer science.