Climate change is widely recognized as one of the most critical global challenges, directly influenced by human activities, particularly the increasing generation and improper management of solid waste. The growing volume of municipal and industrial waste, especially in developing countries, contributes significantly to the emission of greenhouse gases such as carbon dioxide and methane, thereby threatening environmental sustainability. This issue not only exacerbates pollution across air, soil, and water resources but also intensifies public health risks and accelerates climate change impacts. Consequently, effective and sustainable waste management has emerged as a key strategy for mitigating greenhouse gas emissions. This study explores the role of artificial intelligence (AI) in enhancing waste management systems and reducing the environmental and climatic impacts associated with waste generation. Intelligent algorithms have the capability to predict waste generation trends, optimize transportation routes, automate material sorting, and evaluate climate-related policies, all of which contribute to reducing emissions and energy consumption. AI systems have achieved prediction accuracies of up to 98.5% in specific applications, such as monitoring bin fill levels. So, machine learning based predictive models can improve the timing and efficiency of waste collection, thereby decreasing fuel use and pollutant release. The findings indicate that integrating advanced AI technologies with environmental management approaches offers a robust pathway for addressing climate challenges. Such integration can significantly support sustainable development goals by lowering the carbon footprint, improving resource efficiency, and strengthening adaptive capacity to climate change.