Machine Learning-Based Predictive Modeling of Weather Components

The study focuses on the application of artificial intelligence techniques to forecast key weather parameters with improved accuracy and efficiency. Traditional weather prediction methods rely heavily on physical models and large-scale simulations, which, while effective, often face challenges in handling complex, nonlinear relationships among atmospheric variables. Machine learning (ML) offers a data-driven alternative by learning patterns from historical datasets and generating predictive models that can adapt to diverse climatic conditions. This research aims to design and evaluate ML models capable of predicting weather components such as temperature, humidity, thereby contributing to more reliable forecasting systems. The methodology involves collecting extensiveeteorological datasets, preprocessing them to removeinconsistencies, and applying supervised learning algorithms such as random forests. Feature selection techniques are employed to identify the most influential variables, while cross-validation ensures robustness and generalizability of the models. The results demonstrate that ML-based models can capture nonlinear dependencies more effectively than conventional statistical approaches, offering higher accuracy in short term predictions. Keywords: Weather Forecasting, Machine Learning, Climate Change, Trends and challenges etc.