Predicting wildfires has become crucial due to the increasing frequency and intensity of fires driven by climate change. This study focuses on developing advanced predictive models based on the Calabria (Region in Italy) region’s environmental characteristics. We employed comprehensive datasets, including meteorological data, atmospheric gas concentrations, and historical fire occurrence records. Using sophisticated deep learning techniques, we implemented Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and Bi-Directional LSTM (Bi-LSTM) models. These models demonstrated high accuracy in detecting fires, with testing accuracies reaching up to 93.9%. However, predicting fire distance and direction showed moderate success, indicating areas for further improvement. Our research significantly enhances wildfire preparedness and response strategies in the Calabria region. Future work will involve developing hybrid models that integrate various deep learning architectures, incorporating datasets from different years to improve generalizability, and implementing real-time testing to assess performance in dynamic environments. This study provides a valuable tool for regional fire management and mitigation efforts, leveraging cutting-edge technology to protect communities and natural landscapes from the increasing threat of wildfires.