In the current century, natural disasters are occurring on a frequent basis. The global community reacts to these extreme situations in the contemporary era, sharing their experiences and fears on social media. By examining this, the true severity of the disaster can be learned. Individuals use Twitter to express their views on this disaster. These tweets can be analyzed using various big data and natural language processing frameworks to provide a clear picture of the sentiment surrounding that specific disaster. In this study, a variety of text preprocessing methods and machine learning algorithms were employed to produce accurate results. The verdict of this study is to provide insights into how people use social media to convey their experience during these catastrophic situations and thereby mitigate future disaster impacts. In order to evaluate public sentiment and emotional reactions during natural and man-made disasters, this study analyzes social media data to look into sentiment expressed in content related to disaster management. The practical implications of the study's findings for crisis communication include more focused messaging and response strategies that consider the emotional needs and concerns of impacted communities. The present study highlights the significance of utilizing sentiment analysis as a tool to augment disaster resilience, stressing the possibility of constructing more compassionate, knowledgeable, and efficient disaster management frameworks. It sheds light on the impact of disaster type, location, and media coverage on public sentiment and emphasizes the strong correlation that exists between sentiment trends and disaster characteristics.