This study investigates the influence of meteorological parameters on PM2.5 and PM10 concentrations in Rayong’s Pollution Control Zone (PCZ), Thailand, a coastal industrial hub facing significant air quality challenges. Hourly air quality data (2017–2023) from five monitoring stations, alongside meteorological variables (temperature, relative humidity, wind speed, wind direction, rainfall, and atmospheric pressure), were analyzed using Pearson correlation and visualization techniques in R software. Results reveal pronounced seasonal variability, with PM2.5 and PM10 peaking during high humidity (>80%) and moderate temperatures (28–30°C), driven by hygroscopic growth and reduced dispersion in the rainy season. PM2.5 showed a positive correlation with relative humidity (r = 0.42, p < 0.05) and strong negative correlations with sunshine duration (r = −0.898, p < 0.01), minimum temperature (r = −0.796, p < 0.01), and atmospheric pressure (r = −0.771, p < 0.05), indicating enhanced mixing under warmer, sunnier conditions. Conversely, PM10 exhibited a negative correlation with humidity (r = −0.798, p < 0.05) due to wet deposition and a positive correlation with atmospheric pressure (r = 0.782, p < 0.05), reflecting stagnation effects. Both pollutants consistently exceeded WHO guidelines, with post-2019 increases linked to industrial and transboundary sources. Heatmaps and scatter plots highlight spatial and temporal trends, with stations 29T and 31T showing elevated levels. These findings underscore the critical role of meteorological factors in modulating particulate matter, advocating for integrated forecasting models to predict high-risk episodes. Such models can inform targeted interventions to mitigate health risks and support sustainable air quality management in Thailand’s industrial zones. Future research should incorporate real-time data and machine learning to enhance predictive accuracy.