This thesis presents a comprehensive approach to mitigating noise pollution from Unmanned Aerial Systems (UAS) in urban environments through flight path planning using reinforcement learning (RL). The study focuses on Turin, Italy, leveraging its diverse urban architecture to develop a comprehensive model. A detailed 3D occupancy grid map, based on OpenStreetMap data, was created to represent buildings' locations and heights while a population density map was developed to account for demographic variances. The research develops a dynamic noise source model that adjusts noise emission levels based on UAV velocity, ensuring realistic noise impact predictions. Acoustic ray tracing techniques are utilized to simulate noise propagation, accounting for atmospheric absorption and reflections from urban structures, providing a detailed analysis of noise distribution.