An urgent worldwide concern that calls for creative methods of research and solution is climate change. A useful tool for analyzing complicated climate data, machine learning (ML) offers insights into climate trends, enhanced predictions, and anomaly detection. This study examines many machine learning (ML) approaches, including supervised learning, unsupervised learning, and deep learning, in relation to climate data, outlining the main drawbacks, advantages, and potential ramifications. We hope to increase forecasting accuracy, detect extreme weather occurrences, and assist policymakers in developing climate mitigation policies by integrating machine learning (ML) into climate science.