Air pollution is an ever-growing concern across the world as available data suggest that air quality is directly proportional to the quality of life. In particular, the growing need increases accessibility to air quality resources. This study lays out the framework for creating a website-based Air Quality Monitoring System (AQMS), focusing primarily on the frontend part using Laravel. This system seeks to display ported real-time data on air quality pertaining to the Indonesian Air Pollution Standard Index (ISPU) Level, as how the features of the system, which include pollutant indicators, sensor mapping, and responsiveness of the system’s interface. The system also integrates machine learning models for air quality forecasting, enhancing predictive accuracy and enabling proactive environmental management. Designing the front end of the system required user-centered design principles, where feedback provided by the users was integrated into the entire design process. Users were later able to interface with the plexiglass model, which represented the AQMS. The users concluded that the model interface was very user-friendly, hence scoring it 84 out of 100 in Maze Usability Score (MAUS), which indicates the design is easy to use as intended. Usability testing was performed using Maze on three user roles, yielding aligned success rates within acceptable benchmarks and confirming the interface’s usability and effectiveness. From this, it can be deduced that a user-centered design approach sufficed in meeting various users’ experience needs.