Quantum neural networks (QNNs) establish a promising path forward in quantum computing since they use quantum characteristics to optimize performance and learning functionalities. QNNs experience operational barriers due to increasing parameter numbers and quantum state deterioration effects. Tensor networks (TNs) establish an effective mathematical system to overcome difficulties in high-dimensional quantum state representation. The chapter investigates how tensor networks become instrumental for designing quantum neural networks via their ability to simplify the compression of quantum information while minimizing computational demands and improving interpretability levels. The initial section covers basic tensor network principles along with matrix product states (MPS) and projected entangled pair states (PEPS), and tree tensor networks (TTN), their significance in quantum machine learning applications. The text presents an analysis of TN applications which enable the development of QNN architectures that take advantage of quantum entangled information processing. The paper demonstrates how tensor networks serve as circuit parameters to perform state approximation with lower resource consumption. The research investigates training approaches for TN-based QNNs through the evaluation of gradient-based and gradient-free optimization methods. The chapter details implementation examples of tensor-network-based QNNs where they apply to quantum data classification and serve for quantum generative modeling alongside their deployment for hybrid quantum–classical learning tasks. This chapter defines the strengths of TN-based structures through their efficient computation speed in addition to their strong generalization features and their capacity to function under noisy conditions. This reviews unresolved problems in addition to future research opportunities which involve linking fault-tolerant quantum computers to tensor networks and assessing their capacity for near-term quantum device applications. The utilization of tensor networks in quantum neural network design creates a systematic method to solve main obstacles in quantum machine learning that enables practical and scalable quantum artificial intelligence models. The chapter provides researchers with a complete academic foundation regarding the union between tensor networks and quantum neural networks.