Quantum Neural Networks (QNNs) herald a new paradigm in the unison of quantum computing and artificial intelligence, paving the way for transformative machine-learning capabilities and computational issues solving. This chapter probes into QNNs from their basis in classical neural networks and quantum computing. The mathematical foundations important for the design of QNNs-such as linear algebra, quantum gates, and optimization methods-have also been explored in this chapter. Various architectures such as Variational Quantum Circuits (VQCs), Quantum Convolutional Neural Networks (QCNNs), and Recurrent Quantum Neural Networks (RQNNs) have been discussed, each with its own benefits and application domains. It provides comparisons between classical and quantum neural networks, whereby QNNs’ benefits, like speedup and parallelism, are emphasized as well as other disadvantages such as hardware dependency and scaling issues. Training methods featured include quantum gradient descent and natural gradient, while some key challenges that crop up include noise, decoherence, and barren plateaus. Real-world applications of QNNs feature quantum machine learning, optimization, pattern recognition, quantum chemistry, and financial modeling. A case study has been devoted to showcasing a demonstration of QNN implementation and performance. Quantum simulators, such as Pennylane, Qiskit, TFQ, Cirq, PTQ, Microsoft Quantum Development Kit (Q#), contribute to the development and testing of QNNs while dealing with their limitations. Finally, future directions are discussed: hardware improvements, combination with classical AI, and new areas of research. This brief yet compact overview should appraise users of the basics of QNNs, status quo, and possibilities in revolutionizing AI and quantum computing.