In many urban and industrial regions today, maintaining and monitoring air quality has become a major concern. Transportation, energy, fuels, and other forms of pollution all contribute to deteriorating air quality. Given the negative effects of air pollution on both human health and the environment, air quality is of paramount importance in today's world. In this era of technological advancement, the Internet of Things (IoT) emerges as a promising approach for improving healthcare systems. This research describes a novel IoT-integrated air quality detection and alert system that aims to enable real-time monitoring and alerts the user of the situation persisting. The proposed system collects extensive information on air quality using embedded sensors and monitoring equipment. Using machine learning algorithms, the system automatically analyzes collected data to detect Predicting and forecasting air quality is critical for addressing environmental and public health issues. With the increasing impact of air pollution on human health and the environment, accurate and timely air quality forecasting is crucial for managing risks. The goal of this project is to create and implement machine learning methods for air quality prediction and forecasting. The machine learning models developed are applied to real-time data to provide short- and long-term air quality forecasts, allowing for timely public awareness and pollution control measures to be implemented. This study additionally examines the prospects of using future technologies, such as IoT devices, to improve the geographical resolution of air quality predictions. This study advances environmental science and public health by introducing a comprehensive framework for predicting and forecasting air quality using machine learning.