Fire detection and monitoring are essential for mitigating the environmental and societal impacts of wildfire events. Earth Observation (EO) satellites have long played a central role in this domain, with polar-orbiting sensors such as MODIS and VIIRS providing global fire detections twice daily. However, their limited revisit frequency constrains the timeliness of fire alerts. Geostationary satellites, by contrast, offer high temporal resolution through multiple scans per hour, enabling near-real-time fire detection and rapid alerting. Despite this advantage, fire detection algorithms applied to geostationary imagery have predominantly relied on single-timestamp processing, limiting their ability to exploit the full temporal frequency of these observations. In this paper, we propose a multi-temporal approach for active fire detection using data from the Meteosat Third Generation (MTG) Flexible Combined Imager (FCI). The method first applies multi-temporal cloud masking based on the 10.5 μm thermal infrared band, before implementing a multi-temporal fire detection algorithm inspired by the Robust Satellite Techniques (RST) methodology of (Filizzola et al., 2017). To improve detection reliability, a pixel is confirmed as an active fire only after repeated detection across successive timestamps. Compared to the operational single-timestamp MTG FRP-pixel product, the multi-temporal algorithm achieves higher recall with a slight reduction in precision. Additionally, the method demonstrates sensitivity to regional land-use characteristics, excluding persistent industrial heat sources and continuous gas flaring from fire detections. Finally, the algorithm retrieves Fire Radiative Power (FRP) estimates with associated uncertainty bounds, yielding an average uncertainty window of approximately 4.5 MW.