Forest fires in recent years are becoming increasingly devastating for ecosystems, human lives and infrastructures as they follow the climate change impact. In this context the fire monitoring and risk prediction is crucial to support Civil Protection Agencies in charge of the protection of natural ecosystems against fires. Embracing the advancements in remote sensing the fire monitoring task is more and more contributed from automated systems that exploit satellite sensors data, while in the fire risk prediction field, machine learning tends to become the most applied methodology. In this short manuscript we briefly present the development of a daily wildfire risk prediction model based on machine learning techniques and a monitoring system (Forest Fire Information System) for active fires and burn scar mapping that exploits MODIS and VIIRS remote sensing data.