Dengue is of great public health concern regarding the number of people affected. In addition, climate change is associated with the recent spread of dengue fever. Effects of meteorological factors on dengue incidence from 2003 to 2019 in Bangkok city: a model for dengue prediction. Mathematical statistical applied were principal component analysis (PCA), Poisson regression model (PRM), Mann-Kendall (MK), and Sen's slope. PRM considers dengue incidence as the dependent variable and climate variables as independent variables. Meteorological factors are maximum temperature (T-max), minimum temperature (T-min), relative humidity (RH), and rainfall. The rainy season showed a high significant probability of occurrence for new patients. Most trends were statistically significant at 1% for seasonal and annual dengue cases. Another finding was that for every 5-50% of RH variation, there was an average increase (73.33-24,369.19%) in the number of dengue cases. Therefore, RH was the best predictor for increasing dengue incidence in Bangkok. In addition, predictions for dengue incidence were evaluated. This study is a significant result to warn the government, providing valuable information for human health protection.