The earthquakes that occurred in Turkey, Syria, and Morocco in 2023 had an impact, leading to casualties and damage. Infrastructure and widespread human suffering. This research focuses on how social media was utilized during these disasters, specifically examining the sentiment expressed in tweets related to the earthquakes in Turkey and Syria. The study explores techniques such as machine learning, lexical analysis, and hybrid strategies for sentiment analysis. Additionally, it looks into Facebook’s Population During Crisis datasets to gain insights into population movements during emergencies. By defining regions of interest and analyzing location, data from users’ posts on Facebook proves valuable for assessing impact, allocating resources efficiently, and tracking evacuation patterns. The use of this data during the Morocco earthquake is also discussed to show how it complements reports. The discussion section touches upon both the advantages and challenges of using media for disaster communication. While social platforms allow for real-time updates, user engagement, and the amplification of information, they also present obstacles like misinformation spread, network congestion, a lack of verification mechanisms, and emotional repercussions. The paper presents an innovative solution, the employment of AI techniques like machine learning and social media analysis, as both can offer comprehensive insight and improve strategic preparation for better management of natural disasters, especially when responding to earthquakes.