<p>Algal blooms, characterized by the uncontrolled and rapid growth of algae, are mainly instigated by nutrient pollution, climate change, and human activity. Algal blooms pose significant risks to marine ecosystems by causing oxygen depletion, toxic toxin discharge, and water degradation. In addition to their ecological impacts, algal blooms also adversely affect fishing and tourism industries as well as public health. Moreover, algal blooms spread can destroy local economies and cause extensive loss of biodiversity in the affected areas. Algal bloom detection by conventional means is resource-consuming and has limited coverage, which highlights the importance of using automated and effective methods. The present work uses machine learning methods to identify algal blooms from satellite imagery, especially based on SENTINEL-2 satellite images. By applying image segmentation methods, the model can identify spatial patterns indicative of algae presence. The data collection includes high-resolution satellite images with annotations on where the algal blooms are in different marine ecosystems. The study also entails testing various machine learning models and checking how effective they can be when applied in detecting blooms. The findings indicate significant improvement in speed and accuracy of detection, demonstrating the capability of these computational techniques in monitoring environments. Ultimately, this work introduces a novel, automated framework that holds great potential for better management of marine environments by detecting algal blooms early and reducing their environmental and economic effects.</p>