AN INTELLIGENT SYSTEM FOR CLIMATE CHANGE PREDICTION BASED ON MACHINE LEARNING

Climate change creates far-reaching environmental, economic, and social impacts across the globe, making 
reliable and timely prediction tools essential for effective mitigation and adaptation efforts. This research 
introduces a machine-learning-based climate prediction system that utilizes both historical records and real
time environmental data to project climate patterns. The framework combines data preprocessing, feature 
selection, and predictive modeling to uncover complex nonlinear interactions among major climatic factors, 
including temperature, precipitation, carbon emissions, and atmospheric variables. Several machine learning 
models are assessed to identify the most suitable approach for long-term climate forecasting. Results from the 
experiments indicate that the proposed system delivers strong accuracy and consistency when compared with 
conventional statistical methods. Overall, the findings demonstrate how artificial intelligence can strengthen 
climate prediction, inform environmental policy decisions, and support sustainable development goals.