The monitoring of ambient air quality in urban areas is a pressing and pertinent environmental necessity, aimed at the identification of airborne pollutants. This research proposes an ambient air quality monitoring system that employs the use of drones and TTL sensors, primarily the SDS011, to measure PM2.5 and PM10 particles, thereby providing real-time data. The system is based on the utilisation of unmanned aerial vehicles (UAVs) equipped with sensors that collect data from multiple points in a single location at varying elevations. The data transmission is conducted via two slave nodes and a master node based on ESP32 LoRa, facilitating reliable and efficient communication. The research is situated within the context of remote monitoring technology and drone integration for the aerial assessment of ambient air quality. It seeks to provide a comprehensive and detailed coverage that transcends the limitations of existing systems. The data obtained from the tests conducted at various locations within Ambato, Ecuador, was analysed using Minitab software. This allowed for the identification of significant patterns and relationships in air pollution. These patterns and relationships facilitate the formulation of strategies for the effective management of air pollution, enabling the implementation of measures to enhance air quality in Ambato. The system is capable of acquiring data at a rate of once every six seconds, with a latency of 100 to 300 milliseconds. However, as the measurements of the air quality monitoring system are made with an estimated average, this does not affect the behaviour of the system, providing a general overview of the ambient air quality in the analysed sectors.