Introduction: The present study arises in response to the sustained increase in dengue outbreaks in Latin America, with special emphasis on the state of Zulia, Venezuela. This region, composed of 21 municipalities, is highly vulnerable to dengue transmission. Given this scenario, it is essential to have tools that allow early detection of outbreaks and, thus, optimize prevention and public health intervention strategies. The main objective is to develop an early warning system for dengue outbreaks using machine learning (ML) techniques. Materials and methods: Several data sources are integrated: epidemiological information, meteorological parameters, El Niño and La Niña (Niño 3.4 Index), socioeconomic and demographic variables. Two ML models were used: Support Vector Machine for regression (SVM-R) and Gaussian Process Regression (GPR). Results: The predictions obtained showed remarkab...