Dengue is a disease of the tropics and subtropics. It is a vector-borne disease that is transmitted by mosquitoes—Aedes aegypti and Aedes albopictus. Multiple factors contribute to dengue characteristics, of these meteorological variables such as temperature, humidity, rainfall, wind speed, sunlight duration, etc., have an important role in the overall transmission dynamics of dengue. Furthermore, the intensification of climate change has made use of predictive modeling methods imperative in forecasting dengue. PRISMA compliant systematic review search of SCOPUS databases up to December 2024 using pre-specified keywords defining dengue, climate, and modeling strategies was done. Eligibility was screened for candidate studies, data were extracted, and the models were classified. Research findings indicate that Generalized Linear Models (GLMs), Distributed Lag Non-linear Models (DLNMs), Autoregressive Integrated Moving Average (ARIMA), and machine learning models including random forest and neural networks have been widely used for dengue outbreak forecasting. The key meterological predictors of dengue incidence were- temperature values between 25 and 30 °C, moderate rainfall of 4–8 weeks lag, and humidity values above 60%. Further, although machine learning approaches reflect enhanced prediction potential over classical models, they also lack interpretability. The study emphasizes the need for hybrid modeling approaches integrating climate data, remote sensing, and socio-environmental factors for successful dengue forecasting. Climate-driven early warning systems (EWS) must be incorporated into public health policy to minimize dengue outbreaks.