The United Nations’ 13th Sustainable Development Goal (SDG) seeks to address climate change and its implications for society. Understanding the development of the SDGs is crucial for policymakers, particularly when assessing public attitudes towards climate change concerns. This study examines how Twitter users feel about GHG emissions and the actions taken to mitigate climate change. The dataset is studied using natural language processing and machine learning methodologies such as topic modelling and sentiment analysis. Several more analyses, like trend analysis and hashtag analysis, are applied to the dataset to determine the relevance of conversation and the frequency of informed tweets. The dataset is embedded as a 768-dimensional vector, and the K-means clustering technique is used to find the key trends hidden in the dataset. These analyses can help us understand public engagement with GHG emissions and provide us insight into how informed the public is about climate action. The findings show that the overall attitude towards climate change measures is good, and the primary themes of discussion vary, with certain concerns being more frequently discussed than others. The findings are presented across multiple dimensions to help comprehend the relationships between the various groups and pinpoint the most significant terms in the discussion.