Eaton Canyon in California, serves as the focal point for a comprehensive post-wildfire ecological impact assessment. This study employs an approach integrating satellite imagery from the European Space Agency's Sentinel constellation to study a burn area severity. The data include radar and multispectral data, providing a multidimensional view of the affected landscape. The analysis leverages the power of the Random Forest algorithm. Firstly, three widely-used indices the Difference Normalized Burn Ratio (dNBR), Relative Burn Ratio (RBR), and Relative Difference Normalized Burn Ratio (RdNBR) – were calculated and compared based on their accuracy and Kappa Index. Secondly, we developed a machine learning algorithm to create a fire severity map using wildfire indices independently based on their accuracy and Kappa Index and then developed a fusion approach to create a precise fire severity map by classifying the affected area into distinct severity classes. Finally, we compared our results obtained with the analysis of NASA predictions. The results showed a perfect 100% accuracy and Kappa index for all predictions. An area that did not burn due to the topography. Areas classified as low severity showed minimal damage with minimal tree mortality. Low severity to moderate showed regions with partial crown burns and tree mortality. Moderate to high severity areas represented significant tree mortality. High severity illustrated a complete tree mortality and significant loss of vegetation cover. Which may lead to a future work, classification and distribution of vegetation types before and after wildfires using deep earning.