Neural networks play a fundamental role in the domain of artificial intelligence (AI) and machine learning (ML). The new domain uses computer vision, natural language processing, and decision-making systems. The traditional neural networks show ineffectiveness in scalability, optimization, and computational efficiency when provided with large datasets or very complicated patterns. Quantum computing (QC) is an approach that bases quantum physics principles described by superposition, entanglement, and interference. This study is about quantum neural networks (QNNs) that are tested in combining quantum computing and neural networks. The QNNs are expected to maximize the potential for scalability, accuracy, and efficiency to increase performance. This research includes quantum algorithms as Quantum Fourier Transform (QFT), Grover’s technique, and variational quantum circuits. This shows the benefits and challenges in the construction of QNNs through a careful examination of hybrid quantum–classical architectures and fully quantum architectures. The main aim of this research is to understand how quantum computing can aid in the future of neural networks and make it easy in the development of more robust AI systems.