This chapter reviews the role of Tensor Networks (TNs) in the development of scalable Quantum Neural Networks (QNNs), especially their joint hybridization with the neural network architectures in overcoming the quantum computing and machine learning challenges. With the help of the insights gained from recent advancement, we compare ten state of the art models that combine TNs, neural networks, and QNNs in integration. Results reveal that hybrid models highly increase performance, up to 96% accuracy, with a decrease in quantum circuit depth and better behavior of the entanglement entropy scalability. Analyzing graphs shows that tensor networks are particularly excellent in quantum simulations, image classification, and quantum chemistry, being inherently very noise-resistant on NISQ-era hardware. The research indicates some inventive work in Architecture including the Autoregressive Neural TensorNet and Quantum Forest Tensor Network that is more accurate than a machine learning TN or NN model, as well as more efficient. The robustness of QNNs against quantum noise and their potentials of producing high entanglement makes them promising quantum technology and AI applications in the future. This work focuses on how practical synergy exists between the classical and quantum models for constructing the scalable and efficient quantum learning systems.