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While traditional IoT environmental monitoring focuses on real-time data acquisition, the potential for utilizing such telemetry to construct predictive statistical models remains underexplored. This paper presents a multivariate statistical analysis of atmospheric particulate matter (PM) concentration using data harvested from a decentralized IoT sensor network. Utilizing a methodology cantered on Response Surface Methodology (RSM) and 3D surface mapping, this study investigates the non-linear interactions between independent environmental variables—specifically ambient temperature, relative humidity, light intensity, and precipitation—and the dependent variable of air quality (ppm). The dataset, spanning an 8-hour diurnal cycle, reveals complex inter-parameter dependencies that standard linear models fail to capture. Our findings demonstrate that air quality is not merely a function of a single source but is subject to a "compound suppression effect" where humidity and precipitation act as non-linear regulators of particulate concentration. By generating 3D surface response models, we visualize the optimal environmental thresholds that correlate with reduced atmospheric pollutant levels. These results provide a robust statistical framework for the development of predictive edge-computing algorithms capable of forecasting air quality trends based on fluctuating meteorological conditions. This study validates the use of IoT sensor arrays as sophisticated data-mining instruments, bridging the gap between raw telemetry and predictive environmental intelligence.