Machine Learning and Deep Learning Approaches for Weather Forecasting in IoT-Based Systems: A Review - Hasanur Mohammad Firdausi (Raskha Media Group, 2026)
Weather forecasting is important across many fields, including agriculture, transportation, energy, and disaster management. Because meteorological data is not linear or dynamic and changes over time, it is hard to predict the weather. Technological advances have made machine learning and deep learning methods more common for improving the accuracy of weather forecasts. Additionally, connectivity with the Internet of Things (IoT) enables real-time data collection through various environmental sensors. This study conducted a comprehensive literature review of machine learning, deep learning, and hybrid methodologies for IoT-based weather prediction systems. The methodologies analyzed included Random Forest, Support Vector Machine, Artificial Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Convolutional Neural Network, and Transformer. The results showed that deep learning and hybrid models performed better than traditional methods, especially for finding temporal patterns and non-linear correlations. Still, other issues need to be addressed, such as data quality, model complexity, high processing requirements, and limitations on how quickly it can adapt. As a result, combining AI and IoT has significant potential to make weather forecasting systems more accurate, flexible, and timely, especially for early warning systems based on data.