This thesis investigates the application of Quantum Machine Learning (QML) and Deep Learning to the calibration of optical fine-dust sensors, aiming to enhance air quality monitoring within the smart city framework, specifically in Turin. The calibration of these sensors is crucial for deploying a city-wide sensor network that can accurately and efficiently monitor air pollution levels, a step forward in the evolution of smart cities. By leveraging the potential of QML, this work seeks to address the limitations of current sensor calibration methods, thus contributing to the development of more resilient, efficient, and accurate environmental monitoring systems. Smart cities represent the next frontier in urban management, promising enhanced sustainability, livability, and resource efficiency through technology.