Kampelli Sridhar ,Cheluveru Sridhar 03 01 Climate change has intensified the need for reliable and intelligent climate monitoring systems capable of analyzing large and complex environmental datasets Conventional monitoring approaches often struggle with data heterogeneity limited automation and weak forecasting capacity which restrict their effectiveness in supporting climaterelated decisionmaking This study develops an AIdriven climate monitoring framework that brings together predictive modelling and optimisation methods within a unified analytical system The framework employs machine learning and timeseries prediction techniques to forecast temperature variations and emission patterns while optimisation models are used to improve the efficiency and performance of monitoring and energyrelated processes The proposed approach is evaluated using climate datasets under an experimental setting and the results indicate improvements in both prediction accuracy and operational efficiency when compared with baseline models The study demonstrates how AIenabled predictive and optimisation capabilities can strengthen climate monitoring practices and contribute to datainformed environmental planning and sustainability initiatives Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026