Climate change offers many critical challenges to the ecosystems, economies, and communities worldwide, thus making innovative methods in predicting and subsequently mitigating inevitable. Thereafter, machine learning has emerged as the most transformative tool for climate science, doing broad analyses of large data sets, modelling complex interactions, and generating actionable insights. This study examines the position of machine learning in predicting climate change and making environmental sustainability possible-it highlights applications, benefits, and challenges. Applications in the key category comprise climate risk assessment, trend analysis of temperature, identification of flood defence, and resource optimization. Using algorithms like Random Forest Regression and Support Vector Machines, these ML models render high-precision predictions that help policymakers in taking proactive decisions. The study evaluates the performance of algorithms using various performance metrics showing how the Random Forest models prove to be significantly effective in terms of the predictability parameter. These include data scarcity, high computational demands, and model interpretability, which are still significant limiting factors to the full-scale deployment of ML. Such improvements will only be achieved through advancements in data collection and optimization of algorithms. This research will address the issue at hand and underlines the important role of ML in climate science, providing a framework with the enhanced potential for its impacts within the fight against climate change towards environmental sustainability. In this context, the results bring attention to a relatively fast-growing area of research emphasizes on bringing impactful, data-driven solutions to global environmental issues via Machine Learning.