Noise pollution is becoming a serious challenge to urban sustainability and public health. Roadside greenbelts are recognized as an effective, nature-based solution to mitigate these impacts; however, their performance depends on vegetation density, species composition, three-dimensional structure, and spatial relationship to pollution sources. In this study, we employed drone-acquired data—including RGB, multispectral, and LiDAR imagery—to quantitatively model and evaluate the effectiveness of urban roadside greenbelts in improving air quality and reducing noise pollution. Ground and aerial sensors measured pollutant concentrations and ambient noise levels, with observed variations of up to 70% between vegetated and non-vegetated sites. Using 3D modeling tools and vegetation indices such as NDVI, the health and density of vegetation were assessed, while dispersion and acoustic simulations indicated average reductions of 20–25% in pollutant levels and 8–12 dB in noise intensity behind dense vegetation belts. The resulting datasets were integrated in a GIS environment and validated with field observations (R² > 0.85). This research highlights how combining drone-based sensing with computational modeling enables quantitative, data-driven urban planning, offering valuable insights for designing greener, healthier, and quieter cities despite existing technical and regulatory challenges.