The dispersion of air pollutants and the spatial representation of meteorological variables are subject to complex atmospheric local parameters. To reduce the impact of particulate matter (PM2.5) on human health, it is of great significance to know its concentration at high spatial resolution. In order to monitor its effects on an exposed population, geostatistical analysis offers great potential to obtain high-quality spatial representation mapping of PM2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM2.5, relative humidity, temperature and wind speed in the urban district in Cartagena, Colombia. The lack of data due to the scarcity of stations called for an ad hoc methodology, which included the interpolation implementing an ordinary kriging (OK) model, which was fed by data obtained through the inverse distance weighting (IDW) model. To consider wind effects, empirical Bayesian kriging regression prediction (EBK) was implemented. The application of these interpolation methods clarified the areas across the city that exceed the recommended limits of PM2.5 concentrations (Zona Franca, Base Naval and Centro district), and described in a continuous way, on the surface, three main weather variables. Positive correlations were obtained for relative humidity (R2 of 0.47), wind speed (R2 of 0.59) and temperature (R2 of 0.64).