Infusing the scientific state of the art, as identified in the literature, in a modular, scalable, and transferable methodology that focuses on identifying fire risk inside the neighborhoods of citizens, where it matters, and considering and recording the specific critical characteristics for each household, property, road, and part of the infrastructure leads to a complete, high-detail, contemporary and end-to-end fire risk management approach that starts from assessing the risk at a building-block level and achieves the design of evacuation plans and real-time management of extreme events. This type of work has never been implemented in Greece, which is a country that faces extreme forest fire events almost every year, constantly rising in frequency and severity during the last two decades. In particular, Greece has witnessed numerous fire incidents with significant consequences [28]. Among recent incidents, the fire in Mati, Attica, commencing on 23 July 2018, resulted in the tragic loss of 102 human lives and substantial property damage over an area of 1300 hectares [29]. Considering the above, the Prefecture of Attica (Greece) answered the imminent need to counteract this constantly rising threat by financing the research project \"Seismic, Fire Flood Risk Assessment in Attica Region, Greece\". The presented methodology and outputs were developed and produced under this project. The Operational Unit \"BEYOND Centre of Earth Observation Research and Satellite Remote Sensing\", of the Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, of the National Observatory of Athens, is coordinating and leading the first part of this very significant project. In detail, this study proposes an integrated approach that combines advanced techniques, tools, and datasets, such as artificial intelligence/machine learning (AI/ML), remote sensing (RS), geographic information systems (GIS), and field observations. The main focus of this work is to conduct a detailed fire risk assessment and propose efficient management plans (before and during the event) for a building-block-level analysis in selected peri-urban areas. The methodology starts with the assessment of the vulnerability and the economic exposure (in terms of land value) of the studied area, followed by the prediction/identification of possible fire ignition points (through RS and AI/ML) and numerous simulations of the spatiotemporal spread of various fire events based on different scenarios of ignition points and wind characteristics (intensity, direction). The aforementioned combined analysis leads to high-resolution fire risk assessment maps that are also used to plan field campaigns, where critical areas and local high-risk characteristics are identified and recorded. The in situ assessments also work backward as a validation and update procedure of the risk maps. The final step is to design and propose localized and targeted management plans (e.g., evacuation routes, evacuation orders, refuge areas, fire protection zones, etc. ), based on all the preceding work. It is worth noting that while this work incorporates several cutting-edge technologies and diverse datasets, it has also encountered certain limitations. These limitations encompass the use of outdated census data from 2011, data gaps, and constraints in creating the exposure layer. In response to these challenges, we leveraged a combination of field visits, remote sensing data, orthophoto maps, and land value information to address these impediments. Finally, all the project's outputs will be integrated into an interactive web platform that will act as an operational tool under a wider DSS that will assist regional and local authorities to efficiently fight forest fires threats to urban and peri-urban areas. All outputsof the study area were presented to, discussed with, and evaluated by civil protection services, first responders, local authorities, and experts in the field.