Neural computing, as an influential factor of artificial intelligence, is an industry that has managed to achieve an extensive array of innovations. This paper presents an overview of the recent advancements in the field of neural computing, which are focused on state-of-the-art architectures, novel computational paradigms, and their applications in intelligent systems. The paper traces the development of neural networks, from the original artificial neural network (ANN) through deep learning models and on to neuromorphic computing. In other words, the main points of emphasis are breakthroughs in hardware acceleration, hybrid models, and bio-inspired computing, which are responsible for intelligent systems moving to the next generation. This paper also sheds light on how neural computing is ushering in innovations in healthcare, autonomous vehicles, and natural language processing indicating its capacity to influence the decision-making and automation process. It also considers issues related to scalability, interpretability, and ethical issues of neural computing. The goal of the paper is to illustrate the future course of neural computing and its role in advancing AI-driven technologies via an in-depth presentation of the above fields.