<![CDATA[Climate change has emerged as a critical global issue, leading to rising temperatures, extreme weather events, and environmental degradation. Accurate predictions and effective mitigation strategies are essential for minimizing the impacts of climate change on ecosystems, economies, and human health. Mathematical models have proven to be valuable tools in understanding climate dynamics and forecasting future scenarios, enabling policymakers to make informed decisions. This study aims to develop and analyze mathematical models for predicting climate change patterns and evaluating potential mitigation strategies. The focus is on improving the accuracy of climate forecasts and identifying feasible solutions to reduce greenhouse gas emissions and global temperature rise. We employed a combination of differential equations, statistical analysis, and machine learning algorithms to construct climate models. Historical climate data were integrated with greenhouse gas emission projections to simulate future climate scenarios. Additionally, sensitivity analyses were conducted to assess the effectiveness of various mitigation strategies, including renewable energy adoption, carbon capture technologies, and reforestation efforts. The models demonstrate a high degree of accuracy in predicting temperature increases, sea level rise, and the frequency of extreme weather events. Mitigation strategies, particularly those focused on reducing carbon emissions through renewable energy and reforestation, showed significant potential in slowing down global temperature rise by up to 2°C by 2050 under certain conditions. Mathematical modeling provides a powerful approach to predicting climate change and assessing the effectiveness of mitigation strategies. Effective implementation of renewable energy and carbon capture technologies can substantially reduce future climate risks, offering a path toward stabilizing global temperatures. ]]>
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