This paper focuses on the use of quantum computing algorithm where the focus is on the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE) on electrical power systems. Although methods like CLP and GA provide efficient solutions, they have major limitations especially with advanced and complex power systems which are characteristic of the present day utility systems. Machine learning employing quantum mechanics which involves the principles of a quantum is a potential technique to solve these complex tasks with exponential speed-up and improved accuracy than conventional systems. This study systematically defines TO problems for quantum processing, chooses right quantum algorithms and builds corresponding quantum circuits. Other assessments were made using frameworks in quantum computing and compared with the results established in classical models. According to the study, these quantum algorithms are much more efficient than their classical counterparts across two measures: precision and the time taken to make the calculations involved especially when the number of qubits is large. In addition, the proposed framework for scalability provides evidence that quantum algorithms keep applying with improved performance even as the variety and organization of the system increases, compared to exponential time with conventional techniques. However, the study also acknowledges in its current context the quantum hardware weaknesses such as error rate and qubit coherence and pertinence further RD. In conclusion, this paper enriches the existing research because it shows that, with the help of quantum computing, it will be possible to improve the efficiency and reliability of the power system optimization methods in the future.