Abstract: This project aims to develop a real-time forecasting system to predict dengue outbreaks using climatic conditions as key indicators. By leveraging historical dengue case data alongside real-time weather data obtained from public APIs, the system utilizes machine learning techniques, primarily Random Forest Regressor, to model and forecast potential outbreaks. The model processes live rainfall, temperature, and humidity information with lag features to predict the risk level of dengue in different geographical regions. Predictions are visualized through an interactive web-based dashboard, providing timely insights and automated alerts to health authorities and the public for early intervention and proactive mitigation of dengue spread.

Keywords: Dengue Outbreak Forecasting, Machine Learning, Random Forest Regressor, Climatic Conditions, Real-time Weather API, Lag Features, Time-Series Prediction, Interactive Dashboard, Public Health Surveillance, Ensemble Learning

Downloads: | DOI: 10.17148/IARJSET.2026.13121