The impact of technological advancements on the environment has been a growing concern in recent times, with pollution and the greenhouse effect being two of the most significant consequences. As a result, there are now numerous conservation programs being implemented globally to address the issue, with forest conservation and expansion, sustainable agriculture techniques, and the monitoring of habitats for endangered species being a few examples. One such ecosystem that is currently being monitored is the Sundarbans, one of the largest mangrove forests in the world. This vast area is home to thousands of aquatic species, including birds and fish, making it a vital habitat for maintaining biodiversity. However, manual monitoring of such a vast and complex ecosystem is both cost-inefficient and disruptive to the native wildlife. To address this, remote sensing techniques utilizing satellite images and machine learning algorithms offer a non-invasive approach to study and classify mangrove species, as well as detect any signs of stress or damage. The use of high-resolution multispectral data from sources such as LANDSAT, Sentinel, Hyperion, and AVIRIS-NG allows for accurate identification and analysis of even the smallest bands of light [2]. These technologies can provide detailed information on the composition of the forest including vegetation density. This project aims to combine these data with machine learning algorithms to classify and map the most common mangrove species in the Sundarbans and identify any potential stress factors. Remote sensing techniques and machine learning algorithms offer an effective and non-invasive approach to studying and monitoring the mangrove forests of the Sundarbans. These techniques can provide invaluable insights into the composition of the forest, as well as identify potential stress factors and classify the most common species. Lothian Island Wildlife Sanctuary, Dhanchi forest is situated in South 24 Parganas district. By using these technologies, researchers can better understand the complex ecosystem of the Sundarbans and develop more effective conservation strategies to protect this vital habitat for future generations.