Abstract
This research puts forth a pathway to augment weather forecasting accuracy through the formulation of a specialized big data model for IoT applications, with a focus squarely on the analysis of meteorological data. By weaving together Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks into a cohesive hybrid machine learning construct, our objective is to decode the complex spatial and temporal intricacies of weather data. The process spans from gathering data via an IoT setup to preprocessing and leveraging machine learning for predictive analysis. We anticipate that the findings will indicate a significant leap in forecasting accuracy. This study highlights the potential of integrating IoT with cutting-edge analytics to transform meteorological predictions, delivering indispensable insights for sectors that rely on precise weather information.