Indoor air quality plays an important role in students' comfort and academic performance in learning environments. High carbon dioxide (CO₂) concentrations caused by inadequate ventilation can reduce learning concentration. This study aims to develop an Internet of Things (IoT)-based indoor air quality monitoring system using an ESP32 microcontroller and an MQ135 sensor, as well as to analyze the relationship between CO₂ concentration and students' academic performance. The research employed a Research and Development (R&D) approach, with CO₂ concentration data collected every minute from May to October in a classroom environment. The data were processed into monthly averages and compared with students' average academic scores. The results show that the highest average CO₂ concentration occurred in September at 997.78 ppm, accompanied by the lowest average student score of 74.37, while the lowest CO₂ concentration was recorded in July at 712.10 ppm, with an average score of 83.76. The findings indicate a tendency for increased CO₂ concentrations to be followed by a decrease in students' academic performance. The developed system is capable of displaying real-time indoor air quality data through a web-based dashboard and can support the creation of a healthier learning environment.