Nowadays, air pollution represents one of the most serious public health and environmental challenges globally. It adversely affects human well-being, weather patterns, and environmental conditions. This pollution arises from various sources, including hazardous emissions from industries, vehicle exhaust, and the increasing presence of harmful substances and particulates in the atmosphere, leading to air contamination. Pollutants such as carbon monoxide (CO) and carbon dioxide (CO2) contribute to negative impacts on environmental factors like temperature, humidity, and air pressure. Additionally, black carbon particles, emitted through various combustion processes, significantly harm both the environment and human health. Consequently, there is a pressing need to measure and evaluate air quality effectively, facilitating prompt decision-making. In this research, a system was developed that offers a user-friendly interface, providing insights for individuals, communities, and organizations. This empowers them to take informed actions towards reducing air pollution levels. Our system employs a combination of the PM7003 sensor, Raspberry Pi, additional sensors, Internet of Things (IoT) connectivity, cloud computing, and machine learning. It is specifically designed to detect fine particulate matter (PM), including PM2.5, PM1, and PM10 particles, in the air. The system is also equipped with sensors to monitor environmental parameters such as humidity, air pressure, temperature, CO, CO2, and black carbon particles. This robust system enables timely and wise decision-making to mitigate air pollution. Variations in air quality graphs clearly demonstrate the influence of pollutant concentrations on climate change. Our results, comparable to real-world scenarios, were validated against air quality standards and guidelines. Four favourable outcomes were identified from our work. By employing machine learning algorithms, our system can predict air pollution levels with high accuracy, providing reliable forecasts based on historical data and meteorological factors.