SMART WEATHER & DISEASE PREDICTION SYSTEM

Abstract

The field of meteorological informatics plays a crucial role in supporting real-time decision-making and ensuring public health safety in different geographical areas. This research study uses an automated, logic-based system to predict possible disease outbreaks by analyzing live weather data such as temperature, humidity, and specific atmospheric conditions. By collecting accurate, real-time information through the OpenWeatherMap API, the system processes this data with a rule-based inference engine. This engine is designed to identify specific health risks for users. The results show that using live weather data alongside known clinical triggers greatly improves preventive awareness compared to traditional forecasting methods. This research highlights the strong capabilities of Python-based automation in health technology and suggests important future improvements, including better machine learning systems and location tracking for personalized medicine.