Wildfires have significant ecological, social, and economic impacts, release large amounts of pollutants, and pose a threat to human health. Although deep learning models outperform traditional methods in predicting wildfires, their accuracy drops to about 90% when using remotely sensed data. To effectively monitor and predict fires, this project aims to develop deep learning models capable of processing multivariate remotely sensed global data in real-time. This project innovatively uses SimpleGAN, SparseGAN, and CGAN combined with sliding windows for data augmentation. Among these, CGAN demonstrates superior performance. Additionally, for the prediction classification task, U-Net, ConvLSTM, and AttentionConvLSTM are explored, achieving accuracies of 94.53%, 95.85%, and 93.40%, respectively, with ConvLSTM showing better performance. The study focuses on a region in the Republic of Congo, where predictions were made and compared with future data. The results showed a significant overlap, highlighting the model's effectiveness. Furthermore, the functionality developed in this study can be extended to medical imaging and other applications involving high-precision remote-sensing images.