A pivotal method in the field of quantum information processing (QIP) is quantum state tomography (QST), which is mostly used to reconstruct previously unidentified quantum states. However, traditional QST approaches have serious drawbacks due to the enormous number of measurements they require, making them impractical for studying large-scale quantum systems. To address this issue, a new approach is presented by combining Quantum Machine Learning (QML) methods to improve the effectiveness of QST. This work conducts a thorough investigation of various QST techniques, including both classical and quantum approaches. Various QML techniques for QST are used, demonstrating their effectiveness in a variety of simulated and experimental quantum systems, including complex multi-qubit networks. The results of this research support the outstanding prospect of QML-based QST method in achieving very high fidelity levels while drastically reducing the number of measurements needed in comparison to conventional methods. This innovative method has great potential for real-world applications in the field of quantum information processing.