This study employs remote sensing techniques and machine learning models to monitor and analyze Amazon forest fires. The methodology relied on the integration of multi-sensor data, including high-spatial resolution Sentinel-2 and Landsat-8 images, along with thermal data from VIIRS and MODIS sensors. The study used spectral indices such as the Normalized Burn Ratio (NBR) and ΔNBR, in addition to the Random Forest model to classify burn intensity, while a U-Net network was used to segment burned areas. Results showed that integration of optical and thermal data significantly improved early detection efficiency, and machine learning algorithms increased classification accuracy to over 85%.