The ESP32 microcontroller platform [11] is used in this study's creation and deployment of an inventive Internet of Things-based environmental monitoring system for thorough real-time safety and air quality evaluation with integrated machine learning capabilities. When compared to traditional systems, the system's innovative adaptive threshold algorithms, sensor fusion methods, and predictive analytics capabilities greatly increase detection accuracy and lower false alarm rates. In addition to a DHT11 sensor [13][15] for monitoring ambient temperature and humidity, the system incorporates several specialized sensors, such as MQ-5, MQ-9, and MQ-135 gas sensors [10][18] for detecting dangerous gases like liquefied petroleum gas (LPG), carbon monoxide (CO), methane, ammonia, benzene, and smoke particles [9]. The use of a dynamic calibration algorithm, which improves measurement accuracy by 23% over static calibration techniques by compensating for environmental changes and sensor drift in real-time, is a significant advance. The major processing unit is the ESP32 microcontroller [11], which uses its dual-core architecture and 240MHz operating speed together with builtin Wi-Fi and Bluetooth to provide smooth data processing, collection, and the creation of predictive alerts. With response times averaging 18 seconds (65% faster than commercial alternatives), detection accuracy of 95.2%, and false alarm rates decreased to 2.1% from 8.3% in traditional systems across a variety of operating environments, comprehensive performance evaluation shows superior system effectiveness [14]. This system is affordable for residential, commercial, and industrial applications because it uses easily accessible components and reduces costs by 78% when compared to comparable commercial solutions while keeping better performance metrics