This work explores the integration of Quantum Machine Learning (QML) with various applications in development of smart eco system, focusing on its potential to optimize various urban systems and address complex challenges. Mainly, we explored energy management and optimization techniques, considering Quantum Variational Algorithm (QVL). With this study we recognize that the integration of quantum machine learning approaches in smart city applications enhance the sustainability goals with comparison to the classical techniques. Further, we also investigated on how QML algorithms can revolutionize in various aspects in easing transportation, digital public services and policies, security enhancements, and urban planning, offering opportunities to enhance efficiency, sustainability, and resilience in urban environments. However, this study presents several challenges, including scalability limitations, data privacy concerns, and security vulnerabilities in smart cities. Application of QML holds immense promise for urban innovation and transformation. Discussed, various future directions for integrating QML in smart cities applications.